Fusion of Dimension Reduction Methods and Application to Face Recognition

  • Byungjun Son
  • Sungsoo Yoon
  • Yillbyung Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient face recognition. In this paper, we suggest the fusion of Discrete Wavelet Transform(DWT) and Direct Linear Discriminant Analysis (DLDA) for the efficient dimension reduction. The Support Vector Machines (SVM) and nearest mean classifier (NM) approaches are applied to compare the similarity between the similar and different face data. In the experiments, we show that the proposed method is an efficient way of representing face patterns as well as reducing dimension of multidimensional feature.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Byungjun Son
    • 1
  • Sungsoo Yoon
    • 1
  • Yillbyung Lee
    • 1
  1. 1.Division of Computer and Information EngineeringYonsei UniversitySeoulKorea

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