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Amir Benzaoui is an Associate Professor at the Department of Electrical Engineering, University of Bouira (Algeria). He received his BS and MS degrees in computer sciences from the University of Annaba (Algeria) in 2009 and 2011, respectively, and his Ph.D. degree in electronics from the University of Guelma (Algeria) in 1991. His current research interests include Biometrics, Face Recognition, and Image Processing.
Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Sébastien Jacques. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors 2021, 21, 728 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Sébastien Jacques. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors. 2021; 21 (3):728.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Sébastien Jacques. 2021. "Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition." Sensors 21, no. 3: 728.
Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex & Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.
Insaf Adjabi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques. Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition. 2020, 1 .
AMA StyleInsaf Adjabi, Amir Benzaoui, Abdeldjalil Ouahabi, Sebastien Jacques. Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition. . 2020; ():1.
Chicago/Turabian StyleInsaf Adjabi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques. 2020. "Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition." , no. : 1.
Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. Electronics 2020, 9, 1188 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. Electronics. 2020; 9 (8):1188.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. 2020. "Past, Present, and Future of Face Recognition: A Review." Electronics 9, no. 8: 1188.
Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. 2020, 1 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. . 2020; ():1.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. 2020. "Past, Present, and Future of Face Recognition: A Review." , no. : 1.