Figure 4 - uploaded by A. Ouahabi
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Examples of multi-block (MB) image decomposition: (a) 1 × 1, (b) 2 × 2, and (c) 4 × 4. Our idea was to segment the image into equal non-overlapping blocks and calculate the BSIF operator's histograms related to the different blocks. The histogram H2 represents the fusion of the regular histograms calculated for the different blocks, as shown in Figure 5.
Source publication
Adjabi, I.; Ouahabi, A.; Benzaoui, A.; Jacques, S. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors 2021, 21, 728. https://doi.org/10.3390/s21030728
Context in source publication
Context 1
... undefined facial image may be split equally along the horizontal and vertical directions. As an illustration, we can derive 1, 4, or 16 blocks by segmenting the image into grids of 1 × 1, 2 × 2, or 4 × 4, as shown in Figure 4. Each block possesses details about its composition, such as the nose, eyes, or eyebrows. ...
Citations
... The problem of face recognition can be defined as follows: provided that still or video images are given to you, how could you identify or verify individuals in those images [1]. The solution is that easy: by extracting features and comparing patterns from a predefined database of faces. ...
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.
... Using technology, the crop's disease detection procedure can be automated. Artificial intelligence techniques and computer vision systems are most widely used for automating disease detection in plants [14][15][16][17][18][19][20][21]. The use of machine learning has revolutionized computer vision, especially in imagebased detection and classification [22]. ...
Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.
... The problem of face recognition can be defined as follows: provided that still or video images are given to you, how could you identify or verify individuals in those images [1]. The solution is that easy: by extracting features and comparing patterns from a predefined database of faces. ...
p>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.</p
... K-NN is a better choice for face recognition than the current state-of-the-art method. If the data set is larger, K-NN fails to detect anomalies, it struggles with high-dimensional data, and K-NN is sensitive to noise and requires feature scaling [19]. Various optimization algorithms, such as particle swarm optimization (PSO), Cat swarm optimization (CSO), Bat, Cuckoo search algorithm and Whale optimization (WOA), were used for balancing load, energy efficiency and an efficient cloud environment that computed and manipulated data online over the web. ...
... Most published approaches need many training samples to learn a reliable model. However, in real-life situations, the single sample per person (SSPP) problem frequently occurs, such as in passport verification or ID identification, where there is only one training sample from each person stored in the dataset [2]. Face images to be recognized may contain different variations, such as illumination, occlusion, and expression, making SSFR a highly challenging task [3]. ...
... For each texture descriptor, the concepts of Multi-Block (MB) and Color (C) information were exploited as they provide excellent tools for improving the recognition rates. For more details about the MB-C concept, the authors can consult the reference [2]. ...
... The problem of face recognition can be defined as follows: provided that still or video images are given to you, how could you identify or verify individuals in those images [1]. The solution is that easy: by extracting features and comparing patterns from a predefined database of faces. ...
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 problem of face recognition can be defined as follows: provided that still or video images are given to you, how could you identify or verify individuals in those images [1]. The solution is that easy: by extracting features and comparing patterns from a predefined database of faces. ...
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 different results, and the comparison with state-of-the-art methods, show that our approach can be a useful tool for brain cancer detection, diagnosis, and radiotherapy treatment planning. The future direction of our research in brain tumor segmentation must address the limitations of the unsupervised approach by: (1) combining PSO, ANOVA, and a CNN model [84][85][86][87][88][89][90]; (2) using generative adversarial networks [91][92][93][94] to pre-process, colorize, correct, and enhance images before presenting them to the segmentation algorithm. ...
Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.
... Skin recognition know-how demonstrates to generally extremely effective for determining crooks. Once the ideal fit is discovered, a genuine period cropped picture of the determined criminal is conserved, which could be seen by authorized officials for finding as well as monitoring crooks or even for additional exploration [3] - [7]. ...
— Inside India, the identification of crooks is simply by thumbprint identification. This particular identification is constrained, however, as these days majority of crooks are extremely brilliant to keep their thumbprint over the arena. CCTV digital cameras, particularly in private and public places, are set up to offer surveillance tasks with all the arrival of protection technologies. CCTV footage could be utilized to determine the suspects within the arena. Because of minimal software programs designed to immediately identify parallels between the picture inside the footage and also the captured picture associated with a criminal, the law enforces thumbprint identification. An automatic skin recognition device was recommended within this paper for a criminal data source utilizing a recognized Principal Component Analysis strategy. ENN technique will instantly identify your face. In case absolutely no thumbprint provides in the arena, this is going to help police to identify and/or understand the belief on the situation. Outcomes reveal that approximately 89 % on the type in photos could be matched up with the template information.
... CNNs process input images where extract their features and the last layer provides the "voting" of the classes ( in our experiment, there are only 2 classes : crack/non-crack) that we are after. The last stage of the CNN is dedicated to classification [31][32][33][34][35] and gives as output probabilities related the problem treated. For more details the reader can consult the works that we have recently carried out [36][37]. ...
This article proposes an efficient method of auscultation of concrete structures with a non-destructive method using an ultrasound device (Pendit L-200). For the purposes of this work, we have prepared different defects of dosage of the constituents of the concrete, namely sand, different gravels, cement and water, and this with progressively increasing proportions, keeping the others as recommended by the standards. For the defects considered, the transverse intrinsic signals at the center and transversal of the specimens are taken in order to constitute a database. The key element of this method is the multi-resolution analysis based on wavelets. This analysis is coupled with an automatic identification scheme of the types of dosage defects based on deep learning by convolutional neural network (CNN): a technology that is nowadays at the cutting edge of machine learning, especially for all pattern recognition applications.
