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Illumination Robust Face Recognition using Ridge Regressive Bilinear Models  

Shin, Dong-Su (LG전자 전자기술원)
Kim, Dai-Jin (포항공과대학교 컴퓨터공학과)
Bang, Sung-Yang (포항공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.1, 2007 , pp. 70-78 More about this Journal
Abstract
The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.
Keywords
Face Recognition; illumination Robust Face Recognition; Bilinear Model; Ridge Regression; Ridge Regressive Bilinear Model;
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