Video-Based Face Recognition: A Survey - Huafeng Wang; Yunhong Wang; Cao, Yuan (Zenodo, 2009)
During the past several years, face recognition in video has received significant attention. Not only the wide range of commercial and law enforcement applications, but also the availability of feasible technologies after several decades of research contributes to the trend. Although current face recognition systems have reached a certain level of maturity, their development is still limited by the conditions brought about by many real applications. For example, recognition images of video sequence acquired in an open environment with changes in illumination and/or pose and/or facial occlusion and/or low resolution of acquired image remains a largely unsolved problem. In other words, current algorithms are yet to be developed. This paper provides an up-to-date survey of video-based face recognition research. To present a comprehensive survey, we categorize existing video based recognition approaches and present detailed descriptions of representative methods within each category. In addition, relevant topics such as real time detection, real time tracking for video, issues such as illumination, pose, 3D and low resolution are covered. : {"references": ["W.Y. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A\nLiterature Survey, ACM Computing Surveys,Vol:35, 2003.", "G. Aggarwal, A. R. Chowdhury, R. Chellappa, A system identification\napproachfor video-based face recognition, in: 17th International\nConference on PatternRecognition, Vol. 4, 2004, pp. 175- 178.", "B. Moghaddam and A. Pentland, \"Probabilistic Visual Learning for Object\nRepresentation,\" IEEE Trans. Pattern Analysis and Machine\nIntelligence, vol. 19, no. 7, pp. 696-710, July 1997.", "H.A. Rowley, S. Baluja, and T. Kanade, \"Neural Network-Based Face\nDetection,\" IEEE Trans. Pattern Analysis and Machine Intelligence, vol.\n20, no. 1, pp. 23-38, Jan. 1998.", "B. Heisele, T. Poggio, and M. 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