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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Lee, M. S., Chen, M. Y., & Lin, F. S. (2009). Face recognition under variant illumination using PCA and wavelets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5575 LNCS, pp. 341–350). https://doi.org/10.1007/978-3-642-02230-2_35
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