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Proceedings Paper

Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior
Author(s): Cheng-Ta Hsieh; Kae-Horng Huang; Chang-Hsing Lee; Chin-Chuan Han; Kuo-Chin Fan
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Paper 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.

Paper Details

Date Published: 19 December 2017
PDF: 10 pages
Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 1061306 (19 December 2017); doi: 10.1117/12.2299490
Show Author Affiliations
Cheng-Ta Hsieh, National Central Univ. (Taiwan)
Kae-Horng Huang, Chung Hua Univ. (Taiwan)
Chang-Hsing Lee, Chung Hua Univ. (Taiwan)
Chin-Chuan Han, National United Univ. (Taiwan)
Kuo-Chin Fan, National Central Univ. (Taiwan)

Published in SPIE Proceedings Vol. 10613:
2017 International Conference on Robotics and Machine Vision
Chiharu Ishii; Genci Capi; Jianhong Zhou, Editor(s)

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