A Wavelet-Based Face Recognition System Using Partial Information

  • H. F. Neo
  • C. C. Teo
  • Andrew B. J. Teoh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6455)


This paper aims to integrate part-based feature extractor, namely Non-negative matrix factorization (NMF), Local NMF and Spatially Confined NMF in wavelet frequency domain. Wavelet transform, with its approximate decomposition is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. 75% ratio of full-face images are used for training and testing since they contain sufficient information as reported in a previous study. Our experiments on Essex-94 Database demonstrate that feature extractors in wavelet frequency domain perform better than without any filters. The optimum result is obtained for SFNMF of r* = 60 with Symlet orthonormal wavelet filter of order 2 in the second decomposition level. The recognition rate is equivalent to 98%.


Part-based Face Recognition Non-negative Matrix Factorization Wavelet Transform 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • H. F. Neo
    • 1
  • C. C. Teo
    • 1
  • Andrew B. J. Teoh
    • 2
  1. 1.Faculty of Information Science and TehnologyMultimedia UniversityMelakaMalaysia
  2. 2.Biometrics Engineering Research CenterYonsei UniversitySeoulSouth Korea

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