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Resampling for Face Recognition

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Audio- and Video-Based Biometric Person Authentication (AVBPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2688))

Abstract

A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to capture all the facial appearance variations. We utilize the resampling techniques to generate several subsets of samples from the original training dataset. A classic appearance-based recognizer, LDA-based classifier, is applied to each of the generated subsets to construct a LDA representation for face recognition. The classification results from each subset are integrated by two strategies: majority voting and the sum rule. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed approaches improve the recognition accuracy of the classical LDA-based face classifier by about 7 percentages.

This research was supported by NSF IUC on Biometrics (CITeR), at West Virginia University.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science & Engineering, Michigan State University, 48824, East Lansing, MI

    Xiaoguang Lu & Anil K. Jain

Authors
  1. Xiaoguang Lu
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  2. Anil K. Jain
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Editor information

Editors and Affiliations

  1. Center for Vision, Speech and Signal Proc., University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Josef Kittler

  2. Department of Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK

    Mark S. Nixon

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© 2003 Springer-Verlag Berlin Heidelberg

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Lu, X., Jain, A.K. (2003). Resampling for Face Recognition. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_101

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  • DOI: https://doi.org/10.1007/3-540-44887-X_101

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40302-9

  • Online ISBN: 978-3-540-44887-7

  • eBook Packages: Springer Book Archive

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