Journal
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING
Volume 7, Issue 2, Pages 199-214Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219691309002878
Keywords
Wavelet feature extraction; linear discriminant analysis; small sample size problem; face recognition
Funding
- NSF of China [60873168, 60873264, 60803083]
- New Century Excellent Talents of Educational Ministry of China (NCET) [06-0762]
- NSF of Chongqing CSTC [2007BA2003]
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Linear Discriminant Analysis (LDA) is a popular statistical method for both feature extraction and dimensionality reduction in face recognition. The major drawback of LDA is the so-called small sample size (3S) problem. This problem always occurs when the total number of training samples is smaller than the dimension of feature space. Under this situation, the within-class scatter matrix S-w becomes singular and LDA approach cannot be implemented directly. To overcome the 3S problem, this paper proposes a novel wavelet-face based subspace LDA algorithm. Wavelet-face feature extraction and dimensionality reduction are based on two-level D4-filter wavelet transform and discarding the null space of total class scatter matrix S-t. It is shown that our obtained projection matrix satisfies the uncorrelated constraint conditions. Hence in the sense of statistical uncorrelation, this projection matrix is optimal. The proposed method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. Comparing with existing LDA-based methods to solve the 3S problem, our method gives the best performance.
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