Face Recognition under Variant Illumination Using PCA and Wavelets

Authors:
Mong-Shu Lee;Mu-Yen Chen;Fu-Sen Lin
Affiliations:
Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan;Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan;Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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Abstract

In this paper, an efficient wavelet subband representation method is proposed for face identification under varying illumination. In our presented method, prior to the traditional principal component analysis (PCA), we use wavelet transform to decompose the image into different frequency subbands, and a low-frequency subband with three secondary high-frequency subbands are used for PCA representations. Our aim is to compensate for the traditional wavelet-based methods by only selecting the most discriminating subband and neglecting the scattered characteristic of discriminating features. The proposed algorithm has been evaluated on the Yale Face Database B. Significant performance gains are attained.