Past, Present, and Future of Face Recognition: A Review
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...
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doaj-0fee1e5a902a400c8f77cc2a2928d2982020-11-25T01:26:52ZengMDPI AGElectronics2079-92922020-07-0191188118810.3390/electronics9081188Past, Present, and Future of Face Recognition: A ReviewInsaf Adjabi0Abdeldjalil Ouahabi1Amir Benzaoui2Abdelmalik Taleb-Ahmed3Department of Computer Sciences, LIMPAF, University of Bouira, Bouira 10000, AlgeriaDepartment of Computer Sciences, LIMPAF, University of Bouira, Bouira 10000, AlgeriaDepartment of Electrical Engineering, University of Bouira, Bouira 10000, AlgeriaLaboratory of IEMN DOAE, UMR CNRS 8520, University of Valenciennes, 59313 Valenciennes, FranceFace 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.https://www.mdpi.com/2079-9292/9/8/1188face recognitionface analysisface databasedeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Insaf Adjabi Abdeldjalil Ouahabi Amir Benzaoui Abdelmalik Taleb-Ahmed |
spellingShingle |
Insaf Adjabi Abdeldjalil Ouahabi Amir Benzaoui Abdelmalik Taleb-Ahmed Past, Present, and Future of Face Recognition: A Review Electronics face recognition face analysis face database deep learning |
author_facet |
Insaf Adjabi Abdeldjalil Ouahabi Amir Benzaoui Abdelmalik Taleb-Ahmed |
author_sort |
Insaf Adjabi |
title |
Past, Present, and Future of Face Recognition: A Review |
title_short |
Past, Present, and Future of Face Recognition: A Review |
title_full |
Past, Present, and Future of Face Recognition: A Review |
title_fullStr |
Past, Present, and Future of Face Recognition: A Review |
title_full_unstemmed |
Past, Present, and Future of Face Recognition: A Review |
title_sort |
past, present, and future of face recognition: a review |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-07-01 |
description |
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. |
topic |
face recognition face analysis face database deep learning |
url |
https://www.mdpi.com/2079-9292/9/8/1188 |
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