@inproceedings{395f5ae11383453dbdecdbe0c1fa8f41,
title = "Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior",
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.",
keywords = "Face recognition, Fourier transform, histogram equalization, shadow compensation",
author = "Hsieh, {Cheng Ta} and Huang, {Kae Horng} and Lee, {Chang Hsing} and Han, {Chin Chuan} and Fan, {Kuo Chin}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 2017 2nd International Conference on Robotics and Machine Vision, ICRMV 2017 ; Conference date: 15-09-2017 Through 18-09-2017",
year = "2017",
doi = "10.1117/12.2299490",
language = "???core.languages.en_GB???",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Genci Capi and Chiharu Ishii and Jianhong Zhou",
booktitle = "2017 International Conference on Robotics and Machine Vision",
}