Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior

Cheng Ta Hsieh, Kae Horng Huang, Chang Hsing Lee, Chin Chuan Han, Kuo Chin Fan

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.

原文???core.languages.en_GB???
主出版物標題2017 International Conference on Robotics and Machine Vision
編輯Genci Capi, Chiharu Ishii, Jianhong Zhou
發行者SPIE
ISBN(電子)9781510617308
DOIs
出版狀態已出版 - 2017
事件2017 2nd International Conference on Robotics and Machine Vision, ICRMV 2017 - Kitakyushu, Japan
持續時間: 15 9月 201718 9月 2017

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
10613
ISSN(列印)0277-786X
ISSN(電子)1996-756X

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???event.eventtypes.event.conference???2017 2nd International Conference on Robotics and Machine Vision, ICRMV 2017
國家/地區Japan
城市Kitakyushu
期間15/09/1718/09/17

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