Face recognition: challenges, achievements and future directions

• Published in: IET computer vision Vol. 9; no. 4; pp. 614 - 626
• Main Authors: Hassaballah, M, Aly, Saleh
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.08.2015; Wiley
• Subjects: Algorithms; Construction; Degradation; Face recognition; face recognition systems; Failure; Illumination; Law enforcement; Readers; real-world scenarios; Review Article; unconstrained conditions; face recognition systems; face recognition; real-world scenarios; unconstrained conditions
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2014.0084

Face recognition has received significant attention because of its numerous applications in access control, law enforcement, security, surveillance, Internet communication and computer entertainment. Although significant progress has been made, the state-of-the-art face recognition systems yield satisfactory performance only under controlled scenarios and they degrade significantly when confronted with real-world scenarios. The real-world scenarios have unconstrained conditions such as illumination and pose variations, occlusion and expressions. Thus, there remain plenty of challenges and opportunities ahead. Latterly, some researchers have begun to examine face recognition under unconstrained conditions. Instead of providing a detailed experimental evaluation, which has been already presented in the referenced works, this study serves more as a guide for readers. Thus, the goal of this study is to discuss the significant challenges involved in the adaptation of existing face recognition algorithms to build successful systems that can be employed in the real world. Then, it discusses what has been achieved so far, focusing specifically on the most successful algorithms, and overviews the successes and failures of these algorithms to the subject. It also proposes several possible future directions for face recognition. Thus, it will be a good starting point for research projects on face recognition as useful techniques can be isolated and past errors can be avoided.