A SVM-Based Feature Extraction for Face Recognition

  • Peng CuiEmail author
  • Tian-tian Yan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 623)


Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature extraction is particularly important. Linear Discriminant Analysis (LDA) is an effective feature extraction method. However, the traditional LDA cannot solve the nonlinear problem and small sample problem existing in high dimensional space. In this paper, the method of the Support Vector-based Direct Discriminant Analysis (SVDDA) is proposed. It incorporates SVM algorithm into LDA, extends SVM to nonlinear eigenspace, and optimizes eigenvalue to improve performance. Moreover, this paper combines SVDDA with the social computing theory. The experiments were tested on different face datasets. Compared with other existing methods, SVDDA has higher robustness and optimal performance.


Discriminant analysis Face recognition Support vector machine Feature extraction 



This research was funded by science technology research of Heilongjiang provincial education department under Grant No. 11551086.


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Harbin University of Science and TechnologyHarbinChina

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