Abstract
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.
Highlights
Face recognition has gained tremendous attention over the last three decades since it is considered a simplified image analysis and pattern recognition application
We study the performance of deep learning methods under the most commonly used data set: (i) Labeled Face in the Wild (LFW) data set [10] for 2D face recognition, (ii) Bosphorus and BU-3DFE for 3D face recognition
The outcomes of this review show that a substantial boost in this domain’s research occurred 8o. vCeornTtchhleuislsaisoytnfssitveemyaetaicrsr,epvairetwicuplarrolvyidweisthtthhee nadewvenstaotfed-oefe-pthlee-aarrntining afapcpiarol arcehcothgnatithioans oruetspeearfcohrmineda tchoemTmphroeisshtesnpysositpveuemlmaartainccnoremerv.piRueewtecrepnvrtioasvdioivdnaensmcetehstheinondtehsw.isIfinsetaladtdead-orietfi-octlhnee,a-rnalryutmsitnaerteofdaucsaianfldacrpiearcloosgdpnaetictatibosanfsoerrseims(eppaurrobchvliecimnaenandt caormepprreohpeonsseidv.e manner
Summary
Face recognition has gained tremendous attention over the last three decades since it is considered a simplified image analysis and pattern recognition application. With the invention of photography, government departments and private entities have kept facial photographs (from personal identity documents, passports, or membership cards). These collections have been used in forensic investigations, as referential databases, to match and compare a respondent’s facial images (e.g., perpetrator, witness, or victim). Nonintrusive: In contrast to fingerprint or iris images, facial images can quickly be obtained without physical contact; people feel more relaxed when using the face as a biometric identifier. According to a report by the analytical company Mordor-Intelligence [8], the face recognition market was estimated at 4.4 billion dollars worldwide in 2019 and would surpass 10.9 billion in 2025 This technology has already become popular in some countries, such as China.
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