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| Null Space Locality Preserving Discriminant Intrinsicface |
| Lou Song-jiang Zhang Guo-yin |
| College of Computer Science and Application, Harbin Engineering University, Harbin 150001, China |
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Abstract Based on the image differences, Intrinsicface is proposed, which divides the face image into three parts, common facial differences, intrapersonal differences and individual differences, and shows desirable performance. But it does not consider the manifold structure and suffers from the singular problem, which is also called Small Sample Size (SSS) problem. To solve these problems, Null Space Locality Preserving Discriminant Intrinsicface (NSLPDI) is proposed, which makes full use of intrapersonal differences and individual differences and employs the idea of manifold learning so that the similarity in the intra-class is preserved while the separability of samples from different classes is enlarged by discriminant criterion. The optimal feature vectors are extracted from the null space of intrapersonal locality preserving difference scatter matrix, which avoids the singularity and the SSS problem is solved. Experiments on face recognition demonstrate the correctness and effectiveness of the proposed algorithm.
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Received: 28 July 2010
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Corresponding Authors:
Lou Song-jiang
E-mail: lousongjiangac@163.com
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2011, Vol. 33 
