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Face recognition/detection by probabilistic decision-based neural network
Abstract
This paper proposes a face recognition system, based on
probabilistic decision-based neural networks (PDBNN). With technological
advance on microelectronic and vision system, high performance automatic
techniques on biometric recognition are now becoming economically
feasible. Among all the biometric identification methods, face
recognition has attracted much attention in recent years because it has
potential to be most nonintrusive and user-friendly. The PDBNN face
recognition system consists of three modules: First, a face detector
finds the location of a human face in an image. Then an eye localizer
determines the positions of both eyes in order to generate meaningful
feature vectors. The facial region proposed contains eyebrows, eyes, and
nose, but excluding mouth (eye-glasses will be allowed). Lastly, the
third module is a face recognizer. The PDBNN can be effectively applied
to all the three modules. It adopts a hierarchical network structures
with nonlinear basis functions and a competitive credit-assignment
scheme. The paper demonstrates a successful application of PDBNN to face
recognition applications on two public (FERET and ORL) and one in-house
(SCR) databases. Regarding the performance, experimental results on
three different databases such as recognition accuracies as well as
false rejection and false acceptance rates are elaborated. As to the
processing speed, the whole recognition process (including PDBNN
processing for eye localization, feature extraction, and classification)
consumes approximately one second on Sparc10, without using hardware
accelerator or co-processor
... Neural networks (NNs) have gained widespread attention from researchers due to their prevalence in various aspects of our daily lives, such as optimization, signal processing, associative memory, and more [1][2][3][4][5]. Coupled NNs (CNNs) are an unique type of neural network composed of multiple interconnected neural networks that exchange information and collaborate with each other to accomplish tasks [6,7]. ...
... Therefore, the network (38) with the event-triggered controller (4) (̌+̂√ ) and a matrix 0 < = ( ) × such that 1 1 2 ). Then, the network (38) achieves synchronization under the event-triggered controller (4) in the sense of Definition 4.1. ...
... Among the frequently encountered computational-intelligence and information processing models are NNs, designed to emulate the functions of the biological brain or, more broadly, artificial intelligence. Their notable advancements intelligent control systems [4,10], prediction estimation [5], encompass pattern recognition [50], as well as image and signal processing [51,52]. Many of these models are represented by ordinary differential equations (ODEs), assuming well-mixed neurons of interest. ...
... Otomatik yüz tanıma sistemleri, insan-bilgisayar arayüzleri, biyomedikal görüntüleme, güvenlik, kontrol teknolojisi gibi birçok mühendislik uygulamasında odak noktası haline gelmiştir [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Yüz tanıma yaklaşımları genelde, öznitelik-tabanlı yaklaşım [1-4] ve bütünsel yaklaşım [5, 6] olarak iki guruba ayrılır [9]. ...
... Otomatik yüz tanıma sistemleri, insan-bilgisayar arayüzleri, biyomedikal görüntüleme, güvenlik, kontrol teknolojisi gibi birçok mühendislik uygulamasında odak noktası haline gelmiştir [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Yüz tanıma yaklaşımları genelde, öznitelik-tabanlı yaklaşım [1][2][3][4] ve bütünsel yaklaşım [5,6] olarak iki guruba ayrılır [9]. ...
... Yapay sinir ağları (YSA) diğer kural tabanlı sistemlere göre önemli başarım kazanımları sağlamak için istatistiksel ve yapısal bilgiyi kullandıklarından [15], hala yüz tanıma problemlerinde tercih edilen ilk sistem bileşenleri arasındadır [8][9][10][11][12][13][14]. YSA genellikle gradyan iniş algoritmaları ile eğitilir [16] fakat bu algoritmalar yavaş yakınsama probleminden dolayı YSA'nın eğitim süresini artırmaktadır. ...
... So for taking the reading of the motion of the vehicle we connect the various components of the vehicle by the sensor like connecting accelerator pedal and steering wheel and then analyzing the data to assess levels of sleepiness [18]. ECG, EOG, and head motion [19][20]:This is an example of an invasive approach. Some of these decisions required drivers to wear helmets while driving. ...
... c. glasses [0 -no, 1 -yes]; Information on whether the eye picture contains eyeglasses is also supplied for each image (with and without the glasses) d. b. The recognition of face [19] is the first step and then algorithm extract the eyes using the Viola jones [10] eye detection approach and send it to CNN. ...
Drowsy driving is one of the leading causes of traffic accidents all over the world. Driving in a monotonous manner for an extended amount of time without stopping causes tiredness and catastrophic accidents. Drowsiness has the potential to ruin many people’s lives. As a result, a real-time system that is simple to create and configure for early and accurate sleepiness detection is required. In this study, a real-time vision-based system called Driver Drowsiness Detection System has been developed utilizing machine learning. In this study, the Haar Cascade classifier was used to recognize the driver’s face characteristics and functions present in OpenCV library to detect the region of the face. The following step is to examine the open/close state of the eyes, followed by sluggishness depending on the sequence of ocular conditions. The non-intrusive and cost-effective nature of this vision-based driver tiredness detection is its distinguishing attribute.
... There are multiple techniques of face recognition which employs the neural network approach for face authentication. PDBNN is a probabilistic decision-based neural network and it applies the idea of decision based neural network (DBNN) [8,9]. The network approach is not entirely connected with this method. ...
... If the samples are classified to the wrong subnet, then the parameters of the legitimate subnet will be attuned, so that its decision region will be shifted closer to the misclassified sample. PDBNN classification has the benefits of both statistical methods and neural network techniques [8]. It is simple to implement its distributed computing process on parallel machines. ...
The improvement of imaging technology leads us to an era in which user's faces can be acknowledged as a biometric proof of authentication toward an automatic system. Visible imagery is naturally the first option for every facial recognition system. However, visible imagery has two major drawbacks that make the identification systems vulnerable: its dependency on the light source and its incompetence toward face-spoofing attacks. The first part of this study aims to construct a solution against the face-spoofing attack with minimum equipment required. The face recognition solution for smartphones is our hardest use-case because of the uncalibrated camera and unpredictable behaviors of users. From a set of video's frames, the method builds a 3D model of the head using a dedicated reconstruction scheme. This model is highly effective against photo-attack as differences between a real object and an image is truly large. The video attack can be detected by examining the synchronization between the prior motion of the smartphone (explored by motion sensors) and the captured-motion calculated by the 3D reconstruction process. In thermal imagery where the emission source of the spectrum is human's face, the detection of all types of face-spoofing attack is trivial, and the illumination conditions do not affect thermal images. Though, in general, thermal images present less information than visible images. In our second study, we aim to improve the performance of thermal face- recognition method using a 3D model of the vascular network computed from an infrared video.
... Masi et al. [14] summarizes the important algorithms and comparisons in the DeepFace field. Gutta, etc., [15] proposed the hybrid neural network, such as Lawrence [16] by a multistage SOM sample clustering, the convolutional neural network (CNN) [17] is used in face recognition, Lin [18], such as the neural network method based on probabilistic decision, Demers, etc., principal component neural network method is proposed to extract the face image feature, using autocorrelation neural network further compression characteristics, finally MLP is used to realize face recognition. Er et al. used PCA [19] for dimension compression, then extracted features with LDA [20], and then performed face recognition based on RBF. ...
... An example of the triplet in Triplet lossSo for the resulting triad, there are three different scenarios: 1. Easy: refers to the distance from the sample to the positive sample in a triplet is naturally closer than the distance from the negative sample, and the margin is greater than a margin, as shown in formula(17): Hard: Means that the distance from the sample to the positive sample in the triplet is greater than the distance from the negative sample, as shown in formula(18): ...
... Eventually, the ultimate goal of all face recognition systems is to categorize a huge number of data into different classes [35], in fewer words, classifying the features. ...
Human Face receives major attention and acquires most of the efforts of the research and studies of Machine Learning in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation named HFRDWT. The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the Discrete Wavelet Transformation played the dominant role in our system by reducing the raw image resolution to a quarter while maintaining the high level of accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression, extended Cohn-Kanade, Labeled Faces in the Wild datasets, and our new Sudanese Labeled Faces in the Wild dataset showed that our system obtained the least recognition timing (average of 24 milliseconds for training and 8 milliseconds for testing) and acceptable high recognition rate (average of 98%) compared to the other systems.
... 2. Functionality -reinforced Plans: inside methods limited traits like nose, mouth, and eyes are principal of eliminate together with their jobs along with limited data are assumed absorbed in a physical classifier [17]. These processes are decides between 3 varieties: (i) Standard strategies available on styles, facets, activities and collections. ...
Abstract—This paper presents the breakdown of the Unconfined Experience Detection Quality inside an unimpeded face recognition environment. It provides the output from matched image face with recognition from image sequence. It also gives the capabilities similar inequity, light, and expression modification approach. It might be useful for both proof and identity. At this point in time, it will find plenty of way of front view face-recognition. Beforehand few years, for computervision, numbers of face recognition methods have been organized. But, real-planet face-detection tranquil credits an interesting works. The fascination with unconstrained suitable face-recognition is increasing utilizing the explosion of online media for sample community schemes, and video investigation video somewhere come upon assessment is of great meaning. Inside this analysis, it appears to handle popularity inside the predicament of data supposition. It is able to discover a secret expertise by using a Diversified Technique. This work introduces alternatives suggested for unconstrained face recognition quality place and promoting the solution for being used by RFG based face-recognition. Goal of research is RFG concentrated unconstrained face recognition to enhance the exhibition quality. The Method and Simulation of proposed sensing techniques will probably be completed with the use of MATLAB and output with matched query image from save directory.
... Many facial recognition systems employ neural network techniques for facial authentication. The concept of a decision-established neural network is applied in a probabilistic conclusion-based neural network (PDBNN) (DBNN) [27] [28]. This strategy is not quite connected to the network technique. ...
There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
... Many facial recognition systems employ neural network techniques for facial authentication. The concept of a decision-established neural network is applied in a probabilistic conclusion-based neural network (PDBNN) (DBNN) [27] [28]. This strategy is not quite connected to the network technique. ...
There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption
of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our
study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This
work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used
the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the
performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture
detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can
be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
... The ultimate goal of all face recognition systems is to categorize a huge number of data into different classes [36], in fewer words, classifying the features. Support Vector Machines (SVMs) are a group of supervised learning methods used for classification [34], regression and outliers detection. ...
p> Human Face receives major attention and acquires most of the efforts of the researches and studies of Machine Learning (ML) in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging the researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation (DWT) named (HFRDWT). The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network (CNN) to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the DWT played the dominant role in our system by reducing the raw image resolution to quarter while maintaining the high level of the accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression (JAFFE), extended Cohn-Kanade (CK+), Labeled Faces in the Wild (LFW) datasets, and our new Sudanese Labeled Faces in the Wild (SuLFiW) dataset showed that our system obtained the least recognition timing and acceptable high recognition rate compared to the other systems. </p
... The ultimate goal of all face recognition systems is to categorize a huge number of data into different classes [36], in fewer words, classifying the features. Support Vector Machines (SVMs) are a group of supervised learning methods used for classification [34], regression and outliers detection. ...
p> Human Face receives major attention and acquires most of the efforts of the researches and studies of Machine Learning (ML) in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging the researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation (DWT) named (HFRDWT). The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network (CNN) to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the DWT played the dominant role in our system by reducing the raw image resolution to quarter while maintaining the high level of the accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression (JAFFE), extended Cohn-Kanade (CK+), Labeled Faces in the Wild (LFW) datasets, and our new Sudanese Labeled Faces in the Wild (SuLFiW) dataset showed that our system obtained the least recognition timing and acceptable high recognition rate compared to the other systems. </p
... Therefore, selection of classifier has a major impact on the operation of FR system. Numerous other classification models have been developed such as linear regression classifier [20], minimum distance classifier [21], neural networks (NN) [22] and HMM [23]. These distance measures misclassify test images of untrained databases to one of the trained database images. ...
Computational complexity is a matter of great concern in real time face recognition systems. In this paper, four state hidden Markov model for face recognition has been presented whereby coefficients of feature vectors have been curtailed. Face images have been divided into a sequence of overlapping blocks. An observation sequence containing coefficients of eigen values and eigenvectors of these blocks have been used to train the model and each subject is associated with a separate hidden Markov model. The computational complexity of the proposed model has been minimized by employing discrete wavelet transform in the preprocessing stage. Furthermore, singular value decomposition has been employed on face images and a threshold singular value is determined empirically to reject or accept test images. Principal component analysis is used for feature extraction. Accepted test images are classified based on the majority vote criteria using different observation sequences of image features. Experimental findings on Yale and ORL databases in noisy such as Salt and Pepper and noise free environments reveal that the recognition accuracy of the proposed model is comparable to the existing techniques with reduced computational cost.
... However, high accuracy is necessary to ensure that decision making during learning is efficient. ANN has some advantages in terms of learning ability, generalization, and robustness [124] [125]. Recently, studies in the neural networks have increased significantly, especially in Deep Neural Networks (DNNs) [126]. ...
Understanding students’ emotional states during the learning process is one of the important aspects to improve learning quality. Measurements of emotion in an academic setting can be performed manually or automatically using a computer. However, developing an emotion recognition method using an imaging modality that is contactless, harmless, and illumination-independent is challenging. Thermography, as a non-invasive emotion recognition method, can recognize emotion variance during learning by observing the temperature distributions in a facial region. Deep learning models, such as convolutional neural networks (CNNs), can be used to interpret thermograms. CNNs can automatically classify emotion thermograms into several emotional states, such as happiness, anger, sadness, and fear. Despite their promising ability, CNNs have not been widely used in emotion recognition. In this study, we aimed to summarize the previous works and progress in emotion recognition in academic settings based on thermography and CNN. We first discussed the previous works on emotion recognition to provide an overview of the availability of modalities with their advantages and disadvantages. We also discussed emotion thermography potential for the academic context to find if there is any information in the available emotion thermal datasets related to the subjects’ educational backgrounds. Emotion classification using the proposed CNN model was described step by step, including the feature learning illustration. Lastly, we proposed future research directions for developing a representative dataset in the academic settings, fed the segmented image, assigned a good kernel, and built a CNN model to improve the recognition performance.
... The benefits of both the statistical techniques and neural networks are combined in a PDBNN-based biometric identification system, and its shared computing premise is reasonably simple to execute on a parallel computer. This was stated in [12] that the PDBNN facial recognition system could recognize up to 200 persons and obtain a percentage of 96 correct identification rate in around 1 second. Nevertheless, if the amount of people grows, the cost of computing will rise as time goes on. ...
Instagram has become a fastest growing social network in the last three years. It let the users to share their status by uploading images with a descriptive text, a location, and certain hashtags that do not necessarily represent the substance of the pictures. So now Instagram has become a most popular photo-sharing website. While it is a relatively simple service, Instagram's simplicity has contributed to its worldwide success. But unfortunately, some people misuse this website for unethical activities such as sharing false propaganda and fake news, terrorist activities, unethical religious activities, illicit drug distributions etc. Therefore, this work is to recognize the suitable technologies that can be used to retrieve and analyze image data from Instagram such as Demographic analysis, Text analysis, Image analysis, Snowball Technology and some of the face recognition technologies used in iPhone photos , face recognition technologies such as Eigenfaces technology, Neural Networks, Graph Matching, Line Edge Mapping for a system to retrieve and analyze image data from Instagram and to identify the most associated people of a certain Instagram user.
... A face DAR algorithm built-in DL employed in drones can be improved to detect criminals and raise security (Bhattacharyya 2011) DL aims to make the high-level information abstraction by utilizing neural network architecture constructed of multiple non-linear/linear transformations, especially the CNN, which indicates significant advantages. For instance, Lin et al. (1997) proposed a face recognition model by using the probabilistic decision-based deep network. Nair and Cavallaro (2009) presented a robust and accurate method for segmenting and detecting faces, detecting landmarks, and attaining appropriate registration of face patches using the fitting of face information. ...
Unmanned aerial vehicles as known as drones, are aircraft that can comfortably search locations which are excessively dangerous or difficult for humans and take data from bird's-eye view. Enabling unmanned aerial vehicles to detect and recognize humans on the ground is essential for various applications, such as remote monitoring, people search, and surveillance. The current face detection and recognition models are able to detect or recognize faces on unmanned aerial vehicles using various limits in height, angle and distance, mainly where drones take images from high altitude or long distance. In the present paper, we proposed a novel face detection and recognition model on drones for improving the performance of face recognition when query images are taken from high altitudes or long distances that do not show much facial information of the humans. Moreover, we aim to employ deep neural network to perform these tasks and reach an enhanced top performance. Experimental evaluation of the proposed framework compared to state-of-the-art models over the DroneFace dataset demonstrates that our method can attain competitive accuracy on both the recognition and detection protocols.
... Different methods, such as PCA-based eigenfaces [42] and LDA-based Fisherfaces [43] employ the nearest neighbor (NN) classifier and its variants [44]. In a face recognition system, supervised classifiers such as support vector machines (SVM) [45] and neural networks [46] are also proposed. Huang et al. [47,48] developed a novel learning technique for single hidden layer feedforward networks (SLFNs) called the extreme learning machine (ELM), that can be utilized in regression and classification applications [42,[49][50][51]. ...
This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction algorithms, principal component analysis (PCA) and convolutional neural network (CNN). We use and compare the performance of the algorithms K-nearest neighbor (KNN), decision tree, random forest, and CNN. The recognition is done by applying these techniques to the dataset with more than 40K acquired real-time images at different settings such as light level, rotation, and scaling for simulation and performance evaluation. Finally, we recognized faces with a minimum computing time and an accuracy of more than 90%.
... Further work can be achieved by completely automating the facial detection system's frontal vision, which virtually shows the accuracy perfectly. Lin et al. [4] in this work, proposed a face recognition system using PBDNN with the help of eye localization. The system for face recognition also depends on the PDBNN algorithm. ...
Face detection and recognition is a popular issue in computer vision. It has many applications and problems such as pose variation, illumination variation, occlusions which are yet to be solved. Face detection only means detecting whether or not the face of person in the image given. Recognition of the face is the authentication in which the individual is known or recognized. This work has two phases, namely: In the first step, using Viola-Jones-masters algorithm, this work detected the face of the person and using this algorithm we will extract the haar features. We present a face detection and recognition algorithm in the proposed framework that will be accurate in computational terms for large datasets and speed. In the recognition stage, by using the deep learning face embedding process, we will recognize the person by comparing the face with the current novel created dataset of our system and LFW.KeywordsObject detection/Object recognitionVGGDeep learningLFWCNNROIRGB
... 3. Сформувати для кожної підмережі на основі виконаної класифікації множину помилково відхилених зразків [87,88], яка є нерекурентною статичною модульною ІНМ. Кожен модуль являє собою дискримінантну функцію, яка є розподілом Гауса. ...
The monograph considers: methods of digital signal processing
(time and frequency filtering, Fourier transform, transformation
Hartley, cosine transform, wavelet transform); methods of image pre-processing (increasing image contrast, noise reduction and smoothing of images, determination of image brightness differences, threshold image processing, processing of connected components of binary images, geometric image transformations, image compression); methods of selection of informative features of images; clustering methods (center-based, distribution-based, density-based, hierarchical, connectionist); approaches to the recognition of visual images (logical, metric, associative, Bayesian, structural, connectionist and hybrid).
... The simulation parameters are given in Table I. The developed ASO-EHO-H-DNN is compared with the optimization algorithms such as MFO-H-DNN [24], SHO-H-DNN [25], JA-H-DNN [26], and EHO-H-DNN [23] and machine learning models like CNN [27]], Auto Encoder [28], RF [29], SVM [30], KNN [31], and NN [32]. ...
div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div
... The simulation parameters are given in Table I. The developed ASO-EHO-H-DNN is compared with the optimization algorithms such as MFO-H-DNN [24], SHO-H-DNN [25], JA-H-DNN [26], and EHO-H-DNN [23] and machine learning models like CNN [27]], Auto Encoder [28], RF [29], SVM [30], KNN [31], and NN [32]. ...
div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div
... Because of these two remarkable attributes, artifical neural networks have been attracting attention from researchers in face recognition. In 1997, Lin et al. proposed a face detection method using a probabilistic decision-based neural network [63]. The detection accuracy reaches 98.34%. ...
Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.
This paper unreels the old-style issues of human face acknowledgment. The issue of face acknowledgment has been tended to by practically partitioning it into face location and face acknowledgment. Various ways to deal with the issues of face discovery and face acknowledgment were assessed, and were proposed and carried out utilizing the Matlab specialized processing language. The model created is straightforward, quick and precise in obliged conditions. The objective is to apply the model to a specific face and differentiate it from a large number of stored faces, with some real-time variations thrown in for good measure. Various ways have been proposed to tackle this issue. The first step in face recognition is to find a good way to reduce the dimension. The dimensions are reduced to a single dimension when the face is considered to be a matrix of values. In the executed face recognition frameworks. Face location was accomplished utilizing Gabor Channel Component Extraction and ANN in view of picture invariants. Victories are gotten for robotized face discovery and for mechanized face acknowledgment
Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real‐world comprised of far‐field/near‐field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I‐TDM)” technique and traditional “time difference mapping (T‐TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I‐TDM and T‐TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)‐based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating‐based red deer algorithm (DM‐RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM‐RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.
Human faces can reveal not just the human identity, but even demographic characteristics such as ethnicity and gender. Recently, the researchers get the advantages of Deep Learning techniques in developing face recognition systems implemented on both 2D and 3D face datasets. However, the usefulness of Deep learning in analyzing facial features of 3D faces gender, and ethnicity are examined in literature with only three main perspectives: data representation, augmentation, and comparison using the several commonly used format of 3D face representation such as depth images, point clouds, normal maps, triangular mesh, and horizontal disparity images. Many algorithms are implemented by authors on popular 3D datasets including FRGC v2, 3D-Texas, and BU3D-FE. In this work, we highlight the advantages of using the deep learning 3D representation in “race recognition” approaches and refer the researchers to the important related works in this field by comparing them according to their distinguishing metrics and invariant conditions support and the used techniques and datasets.KeywordsRace ClassificationDeep Learning3D face Recognition
The development of advanced computers and upgraded cameras have propelled research toward designing facial recognition systems for various facial representation system in a variety of implementations. Depending on the utility, the facial recognition systems may employ real-time input or offline records. This work proposes the designing and evaluating a CNN-based actual facial detection system. Modern AT&T datasets are used for the initial evaluation of the proposed layout and later extended again for layout of an actual system. Moreover, specifics on how CNN settings are adjusted to judge and improve the suggested system's recognition and reliability have been presented. It is also suggested to tune the parameters using a scientific method to improve the computer's performance. The suggested approach yields maximum recognition accuracies of 98.75% and 98.00% using well-known datasets and real-time inputs, respectively.KeywordsFacial RecognitionConvolutional Neural NetworkDeep Learning
Cloud services in a few decades have received considerable attention resulting in the quest for an efficient infrastructure to support the high demands from clients. In meeting clients’ needs, there is also the need to manage the substantial financial cost associated with energy consumption in these data centres. In this study, an ant colony optimiser was proposed to manage cloudlets’ scheduling effectively. Implementing the optimiser in a CloudSim comparatively reveals significant improvement in energy consumption over the first come,-first serve algorithm initially proposed by the authors of CloudSim.KeywordsAnt colony optimiserColonyCloud computingCloudSimEnergy optimisationCloudletVirtual machine
India’s railways occupy about 1,21,407 km of track. It was noticed in a recent report that 40.7% of injuries were due to railway workplace error and 45.7% were attributed to other humans. Therefore, the manual error of railway workers leads to a significant proportion of rail accidents. We therefore came to the conclusion that one of the explanations could be the testing of the tracks that was carried out manually. Gangmen who inspect the tracks hold heavy equipment weighing up to 8 kg or more. To find any faults or irregularities on the surface of the rails, these gangmen examine the railway tracks closely. They repair it immediately with their equipment until they locate a flaw. Therefore, any flaws on the tracks that could lead to human error and error are likely to be ignored. Our work entails a project focused on developing a railway crack detection system (RCDS) using DC motor, motor controller, Ultrasonic sensor, and Raspberry Pi 3-based module whose application is an excellent approach to detect cracks in the tracks and stopping train derailments of the train. The accuracy and speed to detect small defects in the track are tough. The YoloV5 is among the simplest object detection models for detecting railway track cracks. It is a novel convolutional neural network (CNN) that is used to detect objects with good accuracy The result which shows the overall performance of YoloV5 produces better accuracy to detect the defects.KeywordsDerailmentCrack detectionFlawsMachine learningCNNYolo v5
Cloud services in a few decades have received considerable attention resulting in the quest for an efficient infrastructure to support the high demands from clients. In meeting clients' needs, there is also the need to manage the substantial financial cost associated with energy consumption in these data centers. In this study, an ant colony optimiser was proposed to manage cloudlets' scheduling effectively. Implementing the optimiser in a CloudSim comparatively reveals significant improvement in energy consumption over the First-Come-First-Serve algorithm initially proposed by the authors of CloudSim.
The contribution of a plant is most significant for both human life and nature. The plant diseases affect whole plants, including leaves, stems, fruit, root, and flower. However, conventional approaches enclosed human involvement in classifying and identifying diseases. This process takes more time to complete a task. The main intention of this paper is to effectively develop a deep structured architecture for the detection of plant leaf diseases by introducing intelligent techniques, which have several processing steps. As a major contribution, Adaptive Fuzzy C-Means Clustering (FCM) is adopted for the abnormality segmentation. Moreover, the Improved Deep Neural Network (I-DNN) has achieved the greatest strength in enhancing the performance of plant leaf disease recognition. Here, Newly Updated Moth-Flame Optimization (NU-MFO) is utilised for enhancing the classification efficiency through a valuable objective function. The recommended method achieves higher accuracy rate in the recognition of diseases when compared to the baseline approaches. The precision of the NU-MFO-I-DNN at 85% learning rate is 0.01%, 0.26%, 0.07%, and 0.28% higher than MFO-I-DNN, GWO-I-DNN, SSO-I-DNN, and PSO-I-DNN, respectively.
The current project is primarily concerned with ensuring a secure environment and also free from the hold-ups that occur in your surroundings. This automated surveillance system detects intruders using various devices with software. The major software employed in the current work is Open CV (open-source computer vision). The primary method utilized in the present work is if any person appears in front of the pi camera. It will switch on by observing for probable matches that have previously been stored in our database. If the module finds a match, then it continues to record until an intruder comes. If the face is not recognized then the unknown person’s face will be captured and a snapshot will be sent to the user’s email. The device is developed using Raspberry Pi with a 1.4 GHz quad-core processor, a raspberry pi camera with 12MP high-resolution, and a Wireless dongle to communicate with users’ email. For motion detection, most existing systems employ a Passive Infrared (PIR) motion sensor. Despite its inexpensive cost, such a system has some demerits. For example, as a result of an exceptional situation, such as rapid heating induced by sun exposure, false alarms may be triggered. To increase the efficiency of motion detection, a smart surveillance system is constructed using Open CV on a Raspberry Pi 3 Model B. According to the findings, the built smart surveillance security system using Open CV has a detection rate of 96%, whereas the PIR motion sensorbased security system has a detection rate of 76%.
Face recognition is used as one of the most successful biometric methods due to the availability of advanced resources such as faster processors and higher memory and providing intelligent methods based on the power of these resources. Nevertheless, there are still many challenges in this area. The face plays an important role in the transmission of emotions and carries the characteristics hidden in it, the identity of individuals. Face recognition has been added to some control devices, security, welfare, criminal identification, and many other areas, which is the main motivation for research in this field. In this paper, the DCSFR method is presented to pay attention to the main features of the face such as eyes, lips, mouth, and nose, which is the main novelty of this work, to get higher accuracy or speed than the previously existing methods. In this approach, instead of using general information in face recognition, facial components such as eyes, nose, mouth are separated into another image, and face classification operations (deep learning by convolution neural network) are performed on separated components. The results show that the computational cost with the proposed method is reduced by about 70%. Also, it can be achieved that CNN does not perform as well as the complete picture of the disassembled components.
The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. The software has been made publicly available at https://github.com/MaciejMazurowski/osteoarthritis-classification.
The main concept of this article is to plan for the intelligent rainfall prediction using the combination of deep learning models. The dataset is gathered from the standard publically available dataset concerning the Tamil Nadu state. The collected data is given to the feature extraction, in which few features such as; “minimum value, maximum value, mean, median, standard deviation, kurtosis, entropy, skewness, variance, and zero cross” are extracted. Additionally, the extracted features are applied to the optimal feature formation, in which optimized convolutional neural network (O‐CNN) is employed for the final feature formation. Here, the activation function, count of pooling layer, and count of hidden neurons are tuned with the intention of minimizing the correlation between the selected features. Once the optimal features are selected with less correlation, adaptive long short‐term memory (A‐LSTM) is adopted for the prediction model. Here, the enhancement is concentrated on minimizing the function concerning the error through the optimization of the hidden neurons of A‐LSTM. The improvement of both the deep learning models O‐CNN and A‐LSTM is performed by the improved sun flower optimization (I‐SFO). The research results reveal superior performance to existing techniques that offer novel thinking in rainfall prediction area with optimal rate of prediction.
The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.
We consider the problem of feature-based face recognition in the setting where only a single example of each face is available for training. The mixture-distance technique we introduce achieves a recognition rate of 95% on a database of 685 people in which each face is represented by 30 measured distances. This is currently the best recorded recognition rate for a feature-based system applied to a database of this size. By comparison, nearest neighbor search using Euclidean distance yields 84%. In our work a novel distance function is constructed based on local second order statistics as estimated by modeling the training data as a mixture of normal densities. We report on the results from mixtures of several sizes. We demonstrate that a flat mixture of mixtures performs as well as the best model and therefore represents an effective solution to the model selection problem. A mixture perspective is also taken for individual Gaussians to choose between first order (variance) and second order (covariance) models. Here an approximation to flat combination is proposed and seen to perform well in practice. Our results demonstrate that even in the absence of multiple training examples for each class, it is sometimes possible to infer from a statistical model of training data, a significantly improved distance function for use in pattern recognition
Recent work on face identification using continuous density Hidden
Markov Models (HMMs) has shown that stochastic modelling can be used
successfully to encode feature information. When frontal images of faces
are sampled using top-bottom scanning, there is a natural order in which
the features appear and this can be conveniently modelled using a
top-bottom HMM. However, a top-bottom HMM is characterised by different
parameters, the choice of which has so far been based on subjective
intuition. This paper presents a set of experimental results in which
various HMM parameterisations are analysed
We present a hybrid neural-network for human face recognition
which compares favourably with other methods. The system combines local
image sampling, a self-organizing map (SOM) neural network, and a
convolutional neural network. The SOM provides a quantization of the
image samples into a topological space where inputs that are nearby in
the original space are also nearby in the output space, thereby
providing dimensionality reduction and invariance to minor changes in
the image sample, and the convolutional neural network provides partial
invariance to translation, rotation, scale, and deformation. The
convolutional network extracts successively larger features in a
hierarchical set of layers. We present results using the Karhunen-Loeve
transform in place of the SOM, and a multilayer perceptron (MLP) in
place of the convolutional network for comparison. We use a database of
400 images of 40 individuals which contains quite a high degree of
variability in expression, pose, and facial details. We analyze the
computational complexity and discuss how new classes could be added to
the trained recognizer
Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90% correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach.
An object recognition system based on the dynamic link
architecture, an extension to classical artificial neural networks
(ANNs), is presented. The dynamic link architecture exploits
correlations in the fine-scale temporal structure of cellular signals to
group neurons dynamically into higher-order entities. These entities
represent a rich structure and can code for high-level objects. To
demonstrate the capabilities of the dynamic link architecture, a program
was implemented that can recognize human faces and other objects from
video images. Memorized objects are represented by sparse graphs, whose
vertices are labeled by a multiresolution description in terms of a
local power spectrum, and whose edges are labeled by geometrical
distance vectors. Object recognition can be formulated as elastic graph
matching, which is performed here by stochastic optimization of a
matching cost function. The implementation on a transputer network
achieved recognition of human faces and office objects from gray-level
camera images. The performance of the program is evaluated by a
statistical analysis of recognition results from a portrait gallery
comprising images of 87 persons
The problem of the approximation of nonlinear mapping, (especially
continuous mappings) is considered. Regularization theory and a
theoretical framework for approximation (based on regularization
techniques) that leads to a class of three-layer networks called
regularization networks are discussed. Regularization networks are
mathematically related to the radial basis functions, mainly used for
strict interpolation tasks. Learning as approximation and learning as
hypersurface reconstruction are discussed. Two extensions of the
regularization approach are presented, along with the approach's
corrections to splines, regularization, Bayes formulation, and
clustering. The theory of regularization networks is generalized to a
formulation that includes task-dependent clustering and dimensionality
reduction. Applications of regularization networks are discussed
We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with other state-of-the-art face detection systems are presented; our system has better performance in terms of detection and false-positive rates. This work was partially supported by a grant from Siemens Corporate Research, Inc., by the Department of the Army, Army Research Office under grant number DAAH04-94-G-0006, and by the Office of Naval Research under grant number N00014-95-1-0591. This work was started while Shumeet Bal...
. This paper presents an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face" and "non-face" prototype clusters. A 2-Value metric is proposed for computing distance features between test patterns and the distribution-based face model during classification. We show empirically that the prototypes we choose for our distribution-based model, and the metric we adopt for computing distance feature vectors, are both critical for the success of our system. 1 Introduction Finding human faces automatically in a cluttered image is a difficult yet important first step to a fully automatic face recognition system. It also has many potential applications ranging from surveillance and census systems to humancomputer interfaces. Human face detection is difficult because there can be huge and unpredictable variations in the appearance of face patte...
An algorithm has been developed for the automatic identification of human faces. Because the algorithm uses facial features restricted to the nose and eye regions of the face, it is robust to variations in facial expression, hair style and the surrounding environment. The algorithm uses coarse to fine processing to estimate the location of a small set of key facial features. Based on the hypothesized locations of the facial features, the identification module searches the database for the identity of the unknown face. The identification is made by matching pursuit filters. Matching pursuit filters have the advantage that they can be designed to find the differences between facial features needed to identify unknown individuals. The algorithm is demonstrated on a database of 172 individuals.
The authors apply a hidden Markov model (HMM) and a level-building dynamic programming algorithm to the problem of robust machine recognition of connected and degraded characters forming words in a poorly printed text. A structural analysis algorithm is used to segment a word into sub-character segments irrespective of the character boundaries, and to identify the primitive features in each segment such as strokes and arcs. The states of the HMM for each character are statistically represented by the sub-character segments and the state characteristics are obtained by determining the state probability functions based on the training samples. A level-building dynamic programming algorithm combines word-segmentation and recognition in one operation and chooses the best probable grouping of characters for recognition of an unknown word. The computer experiments demonstrate the robustness and effectiveness of the system for recognizing words formed by degraded and connected characters
We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.
This paper proposes a new approach for extracting features from face images that offer robust face identification against image variations. We combine the K-L expansion technique with two new operations that transform the face pattern into an invariant feature space. The two operations are the affine transformation which yields a standard face view from the input face image, and its transformation into the Fourier spectrum domain, which develops the property of shift-invariance. Although the basic idea of applying the K-L expansion to extract features for face recognition originates from the eigenface approach proposed by Turk and Pentland our scheme offers superior performance due to the transformation into the invariant feature space. The performance of the two schemes for face identification against various imaging conditions is compared.
This paper proposes a new approach for extracting features from face images that offer robust face identification against image variations. We combine the K-L expansion technique with two new operations that transform the face pattern into an invariant feature space. The two operations are the affine transformation which yields a standard face view from the input face image, and its transformation into the Fourier spectrum domain, which develops the property of shift-invariance. Although the basic idea of applying the K-L expansion to extract features for face recognition originates from the eigenface approach proposed by Turk and Pentland our scheme offers superior performance due to the transformation into the invariant feature space. The performance of the two schemes for face identification against various imaging conditions is compared.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
S ummary
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Diss. Nr. 9467 Naturwiss. ETH Zürich.
Thesis (Ph. D.)--Dept. of Computer Science, Stanford University. Bibliography: leaves 161-166.
Supervised learning networks based on a decision-based formulation are explored. More specifically, a decision-based neural network (DBNN) is proposed, which combines the perceptron-like learning rule and hierarchical nonlinear network structure. The decision-based mutual training can be applied to both static and temporal pattern recognition problems. For static pattern recognition, two hierarchical structures are proposed: hidden-node and subcluster structures. The relationships between DBNN's and other models (linear perceptron, piecewise-linear perceptron, LVQ, and PNN) are discussed. As to temporal DBNN's, model-based discriminant functions may be chosen to compensate possible temporal variations, such as waveform warping and alignments. Typical examples include DTW distance, prediction error, or likelihood functions. For classification applications, DBNN's are very effective in computation time and performance. This is confirmed by simulations conducted for several applications, including texture classification, OCR, and ECG analysis.
We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuit filters. A matching pursuit filter is an adapted wavelet expansion, where the expansion is adapted to both the data and the pattern recognition problem being addressed. For identification, the filters find the features that differentiate among faces, whereas, for detection, the filters encode the similarities among faces. The filters are designed though a simultaneous decomposition of a training set into a two-dimensional (2-D) wavelet expansion. This yields a representation that is explicitly 2-D and encodes information locally. The algorithm uses coarse to fine processing to locate a small set of key facial features, which are restricted to the nose and eye regions of the Face. The result is an algorithm that is robust to variations in facial expression, hair style, and the surrounding environment. Based on the locations of the facial features, the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification. The algorithm was demonstrated on three sets of images. The first set was images from the FERET data base. The second set was infrared and visible images of the same people. This demonstration was done to compare performance on infrared and visible images individually, and on fusing the results from both modalities. The third set was mugshot data from a law enforcement application.
The original learning rule of the decision based neural network (DBNN) is very much decision-boundary driven. When pattern classes are clearly separated, such learning usually provides very fast and yet satisfactory learning performance. Application examples including OCR and (finite) face/object recognition. Different tactics are needed when dealing with overlapping distribution and/or issues on false acceptance/rejection, which arises in applications such as face recognition and verification. For this, a probabilistic DBNN would be more appealing. This paper investigates several training rules augmenting probabilistic DBNN learning, based largely on the expectation maximization (EM) algorithm. The objective is to establish evidence that the probabilistic DBNN offers an effective tool for multi-sensor classification. Two approaches to multi-sensor classification are proposed and the (enhanced) performance studied. The first involves a hierarchical classification, where sensor information are cascaded in sequential processing stages. The second is multi-sensor fusion, where sensor information are laterally combined to yield improved classification. For the experimental studies, a hierarchical DBNN-based face recognition system is described. For a 38-person face database, the hierarchical classification significantly reduces the false acceptance (from 9.35% to 0%) and false rejection (from 7.29% to 2.25%), as compared to non-hierarchical face recognition. Another promising multiple-sensor classifier fusing face and palm biometric features is also proposed
This paper proposes a face recognition system based on
decision-based neural networks (DBNN). The DBNN adopts a hierarchical
network structure with nonlinear basis functions and a competitive
credit-assignment scheme. The face recognition system consists of three
modules. First, a face detector finds the location of a human face in an
image. Then an eye localizer determines the positions of both eyes to
help generate size-normalized, reoriented, and reduced-resolution
feature vectors. (The facial region proposed contains eyebrows, eyes,
and nose, but excluding mouth. Eye-glasses will be permissible.) The
last module is a face recognizer. The DBNN can be effectively applied to
all the three modules. The DBNN based face recognizer has yielded very
high recognition accuracies based on experiments on the ARPA-FERET and
SCR-IM databases. In terms of processing speeds and recognition
accuracies, the performance of DBNN is superior to that of multilayer
perceptron (MLP). The training phase for 100 persons would take around
one hour, while the recognition phase (including eye localization,
feature extraction, and classification using DBNN) consumes only a
fraction of a second (on Sparc10)
This paper proposes a face/palm recognition system based on
decision-based neural networks (DBNN). The face recognition system
consists of three modules. First, the face detector finds the location
of a human face in an image. The eye localizer determines the positions
of both eyes in order to generate meaningful feature vectors. The facial
region proposed contains eyebrows, eyes, and nose, but excluding mouth.
(Eye-glasses will be permissible.) Lastly, the third module is a face
recognizer. The DBNN can be effectively applied to all the three
modules. It adopts a hierarchical network structures with nonlinear
basis functions and a competitive credit-assignment scheme. The paper
demonstrates its successful application to face recognition applications
on both the public (FERET) and in-house (SCR) databases. In terms of
speed, given the extracted features, the training phase for 100-200
persons would take less than one hour on Sparc10. The whole recognition
process (including eye localization, feature extraction, and
classification using DBNN) may consume only a fraction of a second on
Sparc10. Experiments on three different databases all demonstrated high
recognition accuracies. A preliminary study also confirms that a similar
DBNN recognizer can effectively recognize palms, which could potentially
offer a much more reliable biometric feature
Given an input vector x, a classifier is supposed to tell which
class is most likely to have produced it. Thus most data classifiers are
designed to have K output nodes corresponding to K classes, {w<sub>i
</sub>: i=1,...,K}. When pattern classes are clearly separated, this
kind of data classifier usually performs very well. A specific model is
the decision-based neural network (DBNN), which is effective in many
signal/image classification applications. This is particularly the case
when pattern classes are clearly separable. However, for those
applications which have complex pattern distribution with two or more
classes overlapping in pattern space, the traditional DBNN may not be
effective or appropriate. For such applications, it is preferable to
adopt a probabilistic classifier. In this paper, we develop a new
probabilistic variant of the DBNN, which is meant for better estimate
probability density functions corresponding to different pattern
classes. For this purpose, new learning rules for probabilistic DBNN are
derived. In experiments on face databases, we have observed noticeable
improvement in various performance measures such as recognition
accuracies and, in particular, false acceptance/rejection rates. Taking
advantage of probabilistic output values of the DBNN, we construct a
multiple sensor fusion system for object classification. In a sense, it
represents an extension of the traditional hierarchical structure of
DBNN
Detection of a (deformable) pattern or object is an important
machine learning and computer vision problem. The task involves finding
specific (but locally deformable) patterns in images, such as human
faces and eyes/mouths. There are many important commercial applications.
This paper presents a decision-based neural network for finding such
patterns with specific applications to detecting human faces and
locating eyes in the faces. The system built upon the proposal has been
demonstrated to be applicable under reasonable variations of orientation
and/or lighting, and with the possibility of eye glasses. This method
has been shown to be very robust against a large variation of face
features and eye shapes. The algorithm takes only 200 ms on a SUN
Sparc20 workstation to find human faces in an image with 320×240
pixels. For a facial image with 320×240 pixels, the algorithm
takes 500 ms to locate two eyes on a SUN Sparc20 workstation.
Furthermore, the algorithm can be easily implemented via specialised
hardware for real time performance. We have applied this technique to
two applications (surveillance system, video browsing) and this paper
provides experimental results. Although we have only shown its
successful implementation on face detection and eye localization, the
proposed technique is meant for more general applications of detection
of any (locally deformable) object
We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for a unimodal distributions) and a multivariate Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition. This learning technique is tested in experiments with modeling and subsequent detection of human faces and non-rigid objects such as hands
A framework called Cresceptron is introduced for automatic
algorithm design through learning of concepts and rules, thus deviating
from the traditional mode in which humans specify the rules constituting
a vision algorithm. With the Cresceptron, humans as designers need only
to provide a good structure for learning, but they are relieved of most
design details. The Cresceptron has been tested on the task of visual
recognition by recognizing 3-D general objects from 2-D photographic
images of natural scenes and segmenting the recognized objects from the
cluttered image background. The Cresceptron uses a hierarchical
structure to grow networks automatically, adaptively, and incrementally
through learning. The Cresceptron makes it possible to generalize
training exemplars to other perceptually equivalent items. Experiments
with a variety of real-world images are reported to demonstrate the
feasibility of learning in the Cresceptron
The feature image and projective image are first proposed to
describe the human face, and a new method for human face recognition in
which projective images are used for classification is presented. The
projective coordinates of projective image on feature images are used as
the feature vectors which represent the inherent attributes of human
faces. Finally, the feature extraction method of human face images is
derived and a hierarchical distance classifier for human face
recognition is constructed. The experiments have shown that the
recognition method based on the coordinate feature vector is a powerful
method for recognizing human face images, and recognition accuracies of
100 percent are obtained for all 64 facial images in eight classes of
human faces
Biometrics is emerging as the most foolproof method of automated personal identification in demand in an ever more automated world. Biometric systems are automated methods of verifying or recognizing the identity of a living person on the basis of some physiological characteristic, like a fingerprint or iris pattern, or some aspect of behavior, like handwriting or keystroke patterns. This paper describes the range of biometric systems in development or on the market including: handwriting; fingerprints; iris patterns; human faces; and speech.< >
The goal of this paper is to present a critical survey of existing
literature on human and machine recognition of faces. Machine
recognition of faces has several applications, ranging from static
matching of controlled photographs as in mug shots matching and credit
card verification to surveillance video images. Such applications have
different constraints in terms of complexity of processing requirements
and thus present a wide range of different technical challenges. Over
the last 20 years researchers in psychophysics, neural sciences and
engineering, image processing analysis and computer vision have
investigated a number of issues related to face recognition by humans
and machines. Ongoing research activities have been given a renewed
emphasis over the last five years. Existing techniques and systems have
been tested on different sets of images of varying complexities. But
very little synergism exists between studies in psychophysics and the
engineering literature. Most importantly, there exists no evaluation or
benchmarking studies using large databases with the image quality that
arises in commercial and law enforcement applications In this paper, we
first present different applications of face recognition in commercial
and law enforcement sectors. This is followed by a brief overview of the
literature on face recognition in the psychophysics community. We then
present a detailed overview of move than 20 years of research done in
the engineering community. Techniques for segmentation/location of the
face, feature extraction and recognition are reviewed. Global transform
and feature based methods using statistical, structural and neural
classifiers are summarized
In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10 3 ) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated. 1 Introduction In recent years considerable progress has been made on the problems of face detection and recognition, especially in the processing of "mug shots," i.e., head-on face pictures with controlled illumination and scale...
Learning human face detection in clut-tered scenes, " in Computer Analysis of Image and Patterns
- K K Sung
- T Poggio
K. K. Sung and T. Poggio, " Learning human face detection in clut-tered scenes, " in Computer Analysis of Image and Patterns. 1995, pp. 432–439.
A fast method for eye localization
- M Fang
- A Singh
- M-Y Chiu
Distortion invariant object recognition in dynamic link architecture
- M Lades
- J Vorbruggen
- J Buhmann
- J Lange
- C Von
- Malsburg