Face recognition using 1DLBP, DWT and SVM

Benzaoui, Amir; Boukrouche, Abdelhani; Doghmane, Hakim; Bourouba, Houcine

doi:10.1109/ceit.2015.7233002

Cited by 14 publications

(12 citation statements)

References 12 publications

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“…Inspired by the original LBP, Benzaoui and Boukrouche [88][89][90] (2013-2014) proposed a new representation of the LBP operator, projected in one-dimensional space, called: one-dimensional local binary pattern (1DLBP), to recognize faces. As shown in Figure 22, they decomposed the feature extraction algorithm into five main steps; first, the image entered was decomposed into several blocks of the same size.…”

Section: Local-texture Approachmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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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.

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Section: Local-texture Approachmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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“…The core implementation and its variants are extensively used in facial image analysis, including tasks as diverse as face detection, face recognition and facial expression analysis. Benzaoui et al [ 43 ] showed that classification tasks which use LBP for feature extraction can improve various statistical procedures, such as principal component analysis (PCA) and discrete wavelet transform (DWT). For example, by using a combination of DWT, LBP and support vector machine (SVM) [ 44 , 45 , 46 ] for classification, it is possible to create a hybrid method for face recognition.…”

Section: Resultsmentioning

confidence: 99%

Privacy-Constrained Biometric System for Non-Cooperative Users

Jahromi

Buch-Cardona

Avots

et al. 2019

Entropy

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With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject's hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

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“…The intra-class variations are larger than the inter-class variations, contributing to low results in recognition. Second, significant alignment errors typically occur when facial landmarks are occluded and degrade recognition rates [170]. Figure 40.…”

Section: Face Recognition and Occlusionmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

Adjabi¹,

Ouahabi²,

Benzaoui³

et al. 2020

Preprint

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114

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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.

show abstract

Face recognition using 1DLBP, DWT and SVM

Cited by 14 publications

References 12 publications

Past, Present, and Future of Face Recognition: A Review

Past, Present, and Future of Face Recognition: A Review

Privacy-Constrained Biometric System for Non-Cooperative Users

Past, Present, and Future of Face Recognition: A Review

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