A comprehensive survey on 3D face recognition methods. (April 2022)

• Record Type: Journal Article
• Authors: Li, Menghan; Huang, Bin; Tian, Guohui
• Is Part Of: Engineering applications of artificial intelligence. Volume 110(2022)
• Publication Date: 2022-04
• Subjects: 3D face recognition -- Deep learning -- Expression -- Pose -- Occlusion -- Survey; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104669
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 3D face recognition (3DFR) has emerged as an effective means of characterizing facial identity over the past several decades. Depending on the types of techniques used in recognition, these methods are categorized into traditional and modern. The former generally extract distinctive facial features (e.g. global, local, and hybrid features) for matching, whereas the latter rely primarily on deep learning to perform 3DFR in an end-to-end way. Many literature surveys have been carried out reviewing either traditional or modern methods alone, while only a few studies are conducted simultaneously on both of them. This survey presents a state-of-the-art for 3DFR covering both traditional and modern methods, focusing on the techniques used in face processing, feature extraction, and classification. In addition, we review some specific face recognition challenges, including pose, illumination, expression variations, self-occlusion, and spoofing attack. The commonly used 3D face datasets have been summarized as well. Highlights: Illustrate and compare 3D face recognition covering traditional and modern methods. Focus on the techniques used in feature extraction and classification. Review specific FR challenges, including pose, expression variations, and occlusion. Summarize commonly used 3D face datasets.