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4: An example of two fingerprint images taken from the same finger (a, b), and the resulting decomposed images (c, d).
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![Figure 2.5: Fingerprint and its frequency image [37].](profile/Mohamed-Wahby-Shalaby/publication/339123712/figure/fig2/AS:856224799141888@1581151308118/Fingerprint-and-its-frequency-image-37_Q320.jpg)
In the first part of this thesis, a novel low-complexity multilevel structural scheme for fingerprint recognition (MSFR) is proposed by first decomposing fingerprint images into regions based on crisp partitioning of some global features of the fingerprints. Then, multilevel feature vectors representing the structural information of the fingerprint...
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Fingerprint-based recognition is widely deployed in different domains. However, the traditional fingerprint recognition systems are vulnerable to presentation attack, which utilizes an artificial replica of the fingerprint to deceive the sensors. In such scenarios, Fingerprint Liveness Detection (FLD) is required to ensure the actual presence of a...
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... Preprocessing is used to segment a section of the palm print picture for feature extraction. Feature extraction derives valuable characteristics from a preprocessed palm print [37]. A database stores registered templates, and a matcher examines the characteristics of two palm prints, as seen in Figure 3. Table 2: System Type Techniques [18,20] NO Name Description Figure 1 Detection ...
... Identification The capacity to distinguish one person from another. Figure 3: four steps in a standard palm print recognition system [37] There are two steps to the process: training (enrollment) and testing (Recognition) [13]. During the training phase (enrollment), a biometric sensor or reader collects each palm to produce a digital picture. ...
For over 15 years, palmprint identification technology has been developed and tested on a range of image resolutions (high and low). This study demonstrates the numerous varieties of palmprints and the difficulties associated with the palmprint recognition method. Furthermore, we go over the step-by-step process of developing a palmprint biometrics system, starting with image acquisition, preprocessing, feature extraction, and matching, as well as a summary of palmprint databases and their characterizations, as well as some palmprint recognition techniques and research works related to palmprint biometrics purposes. This paper focuses on comparing the types of systems in terms of deep learning, machine learning, and systems that require learning.
... Shalaby [59] suggested that the computational cost can be reduced significantly by assigning the clusters' centroids as the 1 st step of the traditional FCM algorithm for fingerprint recognition application. This (10) (11) (12) assumption is tested for the CVRP by forcing the FCM algorithm to start with assigned initial centroids by three methods. ...
Fuzzy C-Means (FCM) clustering technique is among the most effective partitional clustering algorithms available in the literature. The Capacitated Vehicle Routing Problem (CVRP) is an important industrial logistics and managerial NP-hard problem. Cluster-First Route-Second Method (CFRS) is one of the efficient techniques used to solve CVRP. In CFRS technique, customers are first divided into clusters in the first phase, then each cluster is solved independently as a Traveling Salesman Problem (TSP) in the second phase. This research is concerned with the clustering phase of CFRS, and TSP is then solved using a traditional optimization method. Three supervised FCM based techniques are proposed to solve the clustering phase at reduced cost via centroids (pre-FCM) initialization phase. The proposed pre-FCM initialization techniques are developed to be problem dependent. Hence, three initialization techniques are first developed using K-means technique, spatially equally distributed, and demand weighted center of mass. Then, a modified demand weighted fuzzy c-means objective function is employed to assign customers to clusters. To compare the performance of the proposed supervised FCM techniques, forty-two CVRP benchmark problems are solved using the traditional fuzzy C-means algorithm and the developed algorithms. Extensive comparisons are conducted between the traditional fuzzy C-means algorithm, the three proposed initialization techniques, and other fuzzy C-means techniques available in the literature. Results show that the proposed three initialization techniques are efficient in terms of solution quality and computational cost.
... Over decades massive research work have been developed using artificial intelligence (AI), and deep learning techniques to solve efficiently different complex problems [10][11][12][13][14]. Recently, there are several human emotion recognition schemes have been developed based on AI, and deep learning techniques [8,15,16]. ...
... To create chunks, 5 consecutive frames are combined, which results in 12 chunks during the 60s of each video (60/5 = 12). This means that in the fusion stage, each chunk in the EEG domain is combined with its corresponding chunk in the face domain since the same number for chunks (12) exists in the 60 s. Fig. 6 describes the fusion process on the time scale of the input video, and signal. ...
Nowadays, human emotion recognition is a mandatory task for many human machine interaction fields. This paper proposes a novel multi-modal human emotion recognition framework. The proposed scheme utilizes first the 3D-Convolutional Neural Network (3D-CNN) deep learning architecture for extracting the spatio-temporal features from the electroencephalogram (EEG) signals, and the video data of human faces. Then, a combination of data augmentation, ensemble learning techniques is proposed to get the final fusion predictions. The fusion of the multi-modalities in the proposed scheme is carried out using data, and score fusion methods. Hence, three human recognition approaches are built to achieve the proposed goal. They are namely EEG-based emotion recognition approach, face-based emotion recognition approach, and fusion-based emotion recognition approach. For the EEG approach, the 3D-CNN is used to get the final predictions of the EEG signal. For the face approach, the Mask-RCNN object detection technique in combination with OpenCV libraries are first utilized to extract the exact face pixels with emotional content. Then, the Support Vector Machine (SVM) classifier is utilized to classify the 3D-CNN output features of the face chunks. For the fusion-based emotion recognition approach, two fusion techniques are experimented; bagging, and stacking. It is found that the stacking technique gives the best accuracy, and achieves recognition accuracies 96.13%, and 96.79% for valence, and arousal classes respectively using the grid search ensemble learning technique due to transferring the weights from the EEG, and the face approaches to the fusion-based emotion recognition approach. The proposed approach outperforms recent works in multi-modal emotion recognition field.
... It is clearly seen from the previous literature that the real-world limitations, such as noise, shadowing, and variations in cameras, are significant factors that make the problem of localizing watermarks more challenging. For decades, fuzzy logic and fuzzy-based techniques have been used successfully to deal with a wide variety of challenges in different research areas [25][26][27]. In addition, it is well known that the Fuzzy C-Means classifier is an efficient classifier that has been used in different research areas to improve the accuracies of clustering problems [27,28]. ...
... For decades, fuzzy logic and fuzzy-based techniques have been used successfully to deal with a wide variety of challenges in different research areas [25][26][27]. In addition, it is well known that the Fuzzy C-Means classifier is an efficient classifier that has been used in different research areas to improve the accuracies of clustering problems [27,28]. In this research work, a novel FCM-based approach for locating visible video watermarks is proposed. ...
... Since there are no well-defined measuring criteria for measuring the performance of the watermark localization approach, we used the measurements commonly used in the literature to measure the performance of the proposed approach. As in [27], the performance can be measured in terms of the next three criteria. ...
The increased usage demand for digital multimedia has induced significant challenges regarding copyright protection, which is the copy control and proof of ownership. Digital watermarking serves as a solution to these kinds of problems. Among different types of digital watermarking, visible watermarking protects the copyrights effectively, since the approach not only prevents pirates but also visually proves the copyright of the broadcasted video. A visible watermark could be in any location on the frame (corner, center, diagonal, etc.). In addition, it could either completely or partially disappear for some frames. The same video also might have multiple watermarks. In order to strengthen the techniques of adding visible watermarks, there is a need to discover the weakness of the used watermarks. Since the major step of attacking a visible watermark is to locate it accurately, in this paper, a Fuzzy C-Means (FCM)-based approach is proposed to locate visible watermarks in video. Broadcasting channels are used to utilize video logos, which can be considered as a form of visible watermark that represents a trademark or symbol to declare the intellectual property ownership. In general, a high-standard video watermark has such properties as a clear background with distinctive shape without additional texture obscuring the watermark area. In addition, the probability of the logo appearing in the four corners of the video frames is higher than in the center. Based on these common properties of the video watermark, the proposed scheme locates the visible watermark using the Fuzzy C-Means technique without any prior information. The proposed technique has two stages: the first stage is positioning, and the second is masking (extracting the watermark mask). Due to real-world limitations such as noise, shadowing, and variations in cameras, the positioning stage is developed by employing gradient and Fuzzy C-Means classifier techniques. By using the dilation and erosion operators, the masking stage is developed to extract the watermark mask. Using a set of trademark videos, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed FCM-based technique is able to achieve higher accuracy at a reasonable computational cost in comparison to the most related and recent published work. The proposed technique could locate different watermarks with high symmetry in their pattern, even if they appeared mutually in the same location. Still, it will be a challenge if the symmetry is low between used watermarks in the same location.
... In this scheme, a new 2D based region growing algorithm is proposed to be able to achieve more accurate segmented arteries at a reasonable computational cost. Impressed by the ability of fuzzy C-means clustering (FCM) technique to meet different challenges in different research fields [18], our new 2D based region growing algorithm uses the FCM for determining the seed points. This new growing algorithm is used to produce a 3D volume binary data, which leads to faster extraction process of centerlines using skeletonization approach [19] . ...
... Based on the normal anatomy of the coronary arteries, the range of ӨL for the left Ostiaϵ [-90º, 45º] and for the right Ostia ϵ [45º, 135º]. FCM [18] used here to detect optimum left ostia point and right point in Ө range from -90º to 135º with two centers. It is based on minimization of the following objective function: ...
Coronary arteries segmentation and centerlines extraction is an important step in Coronary Artery Disease diagnosis. The main purpose of the fully automated presented approaches is helping the clinical non-invasive diagnosis process to be done in fast way with accurate result. In this paper, a hybrid scheme is proposed to segment the coronary arteries and to extract the centerlines from Computed Tomography Angiography volumes. The proposed automatic hybrid segmentation approach combines the Hough transform with a fuzzy-based region growing algorithm. First, a circular Hough transform is used to detect initially the aorta circle. Then, the well-known Fuzzy c-mean algorithm is employed to detect the seed points for the region growing algorithm resulting in 3D binary volume. Finally, the centerlines of the segmented arteries are extracted based on the segmented 3D binary volume using a skeletonization based method. Using a benchmark database provided by the Rotterdam Coronary Artery Algorithm Evaluation Framework, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed hybrid scheme is able to achieve a higher accuracy, in comparison to the most related and recent published work, at reasonable computational cost.
Omni mobile robots are one of the mobile robots that interact with humans in many areas where it is needed to be collaborative and accurate. Committing robotics with artificial intelligence-based controllers became nowadays mandatory for more association of these robots with distinct environments. This paper proposes the distinction of the 3WD Omni Vision feedback model between Simscape and actual information to obtain a surmised model. Study applying some artificial control procedures on this model for path planning and speed control as the artificial neural system and PSO optimization techniques for PID error and contrast these outcomes and compare them with conventional PID and Fuzzy Control techniques.
Fuzzy C-Means clustering is among the most successful clustering techniques available in the literature. The capacitated vehicle routing problem (CVRP) is one of the most studied NP-hard problems. CVRP has attracted the attention of many researchers due to its importance within the supply chain management field. This study aims to develop a fuzzy c-means clustering heuristic to efficiently solve the CVRP with large numbers of customers by using cluster-first route-second method (CFRS). CFRS is a two-phase technique, where in the first phase customers are grouped into, and in the second phase each cluster is solved independently as a traveling salesman problem (TSP). This work is concerned the clustering phase of the CFRS. The second phase of the CFRS method is solved using traditional optimization software. A modified demand weighted fuzzy c-means clustering algorithm is developed to solve the clustering phase. Twenty-five instances are solved to evaluate the efficiency of the proposed algorithm. Some of them are large instances with more than 500 customers. Promising results in terms of accuracy and processing time are obtained.
In recent years, the massive growth in the scale of data is being a key factor in the needed data processing approaches. The efficiency of the algorithms of knowledge extraction depends significantly on the quality of the raw data, which can be improved by employing preprocessing techniques. In the field of energy consumption, the forecasting of power cost needed plays a vital role in determining the expected profit. To achieve a forecasting with higher accuracy, it is needed to deal with the large amount of data associated with power plants. It is shown in the literature that the use of artificial neural networks for the forecast electric power consumption and show short term profit operation is capable of achieving forecasting decisions with higher accuracy. In this research work, a neuro-fuzzy based approach for energy consumption and profit operation forecasting is proposed. First, the main influential variables in the consumption of electrical energy are determined. Then, the raw data is pre-processed using the proposed fuzzy-based technique. Finally, an artificial neural network is employed for the forecasting phase. A comparative study is conducted to compare between the proposed approach and the traditional neural networks. It is shown that the achieved forecasting accuracy of the proposed technique is better than what achieved by employing only the neural network.
