Two-Dimensional Discriminant Transform Based on Scatter Difference Criterion for Face Recognition

  • Cai-kou Chen
  • Jing-yu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


In this paper, a novel image discriminant analysis method, coined two-dimensional discriminant transform based on scatter difference criterion (2DSDD), is developed for image representation. The proposed 2DSDD scheme adopts the difference of both between-class scatter and within-class scatter as discriminant criterion. In this way, the small sample size problem usually occurred in the traditional Fisher discriminant analysis (LDA) is essentially avoided. In addition, the developed method directly depends on image matrices. That is to say, it is not necessary to convert the image matrix into high-dimensional image vector like those conventional linear discriminant methods prior to feature extraction so that much computational time would be saved. Finally, the experimental results on the ORL face database indicate that the proposed method outperforms Fisherfaces, the standard scatter difference discriminant analysis, not only in the computation efficiency, but also in its recognition performance.


Face Recognition Image Vector Image Matrice Image Feature Extraction Discriminant Criterion 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cai-kou Chen
    • 1
    • 2
  • Jing-yu Yang
    • 2
  1. 1.Department of Computer Science and EngineeringYangzhou UniversityYangzhouChina
  2. 2.Department of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina

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