Feature extraction based on Laplacian bidirectional maximum margin criterion

Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local...

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Bibliographic Details
Published inPattern recognition Vol. 42; no. 11; pp. 2327 - 2334
Main Authors Yang, Wankou, Wang, Jianguo, Ren, Mingwu, Yang, Jingyu, Zhang, Lei, Liu, Guanghai
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.11.2009
Elsevier
Subjects
Applied sciences
Bidirectional
Cryptography
Exact sciences and technology
Face recognition
Feature extraction
Image processing
Information, signal and communications theory
Laplacian
Miscellaneous
MMC
Pattern recognition
Signal and communications theory
Signal processing
Telecommunications and information theory
Feature extraction
MMC
Bidirectional
Face recognition
Laplacian
Biometrics
Performance evaluation
Discriminant analysis
Bidirectional link
Image processing
Pattern recognition
Learning
Information protection
Database
Signal processing
Automatic recognition
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Summary:Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples and the structural information embedding in the images. In this paper, we develop a novel criterion, namely Laplacian bidirectional maximum margin criterion (LBMMC), to address the issue. We formulate the image total Laplacian matrix, image within-class Laplacian matrix and image between-class Laplacian matrix using the sample similar weight that is widely used in machine learning. The proposed LBMMC based feature extraction computes the discriminant vectors by maximizing the difference between image between-class Laplacian matrix and image within-class Laplacian matrix in both row and column directions. Experiments on the FERET and Yale face databases show the effectiveness of the proposed LBMMC based feature extraction method.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2009.03.017