Journal
NEURAL PROCESSING LETTERS
Volume 37, Issue 3, Pages 355-375Publisher
SPRINGER
DOI: 10.1007/s11063-012-9252-y
Keywords
Feature extraction; Dimensionality reduction; Face recognition; Finger knuckle print recognition; Linear regression classification
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Funding
- National Science Foundation of China [90820306, 60873151, 60973098, 61005008]
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Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.
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