19 December 2017 Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior
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Proceedings Volume 10613, 2017 International Conference on Robotics and Machine Vision; 1061306 (2017) https://doi.org/10.1117/12.2299490
Event: Second International Conference on Robotics and Machine Vision, 2017, Kitakyushu, Japan
Abstract
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.
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Cheng-Ta Hsieh, Cheng-Ta Hsieh, Kae-Horng Huang, Kae-Horng Huang, Chang-Hsing Lee, Chang-Hsing Lee, Chin-Chuan Han, Chin-Chuan Han, Kuo-Chin Fan, Kuo-Chin Fan, } "Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior", Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 1061306 (19 December 2017); doi: 10.1117/12.2299490; https://doi.org/10.1117/12.2299490
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