DocumentCode :
419503
Title :
Illumination and expression invariant face recognition with one sample image
Author :
Chen, Shaokang ; Lovell, Brian C.
Author_Institution :
Sch. of Inf. Technol. & Electr. Eng., Queensland Univ., St. Lucia, Qld., Australia
Volume :
1
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
300
Abstract :
Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This problem becomes more serious in applications when only one sample images per class is available. In this paper, we present a linear pattern classification algorithm, adaptive principal component analysis (APCA), which first applies PCA to construct a subspaces for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability. This technique performed well under variations in lighting conditions. To produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. This method is evaluated on the Asian face image database. Experiments show that APCA outperforms PCA and other methods in terms of accuracy, robustness and generalization ability.
Keywords :
emotion recognition; face recognition; image representation; lighting; pattern classification; principal component analysis; visual databases; Asian face image database; PCA; adaptive principal component analysis; covariance matrices; face recognition; facial expressions; illumination; image representation; lighting condition; linear pattern classification algorithm; Classification algorithms; Eyes; Face recognition; Image databases; Image representation; Information technology; Lighting; Pattern classification; Pattern recognition; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1334112
Filename :
1334112
Link To Document :
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