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Robust Face Recognition with Light Compensation

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Advances in Multimedia Information Processing — PCM 2001 (PCM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2195))

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Abstract

This paper proposes a face recognition method which is based on a Generalized Probabilistic Descent (GPD) learning rule with a three-layer feedforward network. This method aims to recognize faces in a loosely controlled surveillance environment, which allows (1) large face image rotation (on and out of image plane), (2) different backgrounds, and (3) different illumination. Besides, a novel light compensation approach is designed to compensate the gray-level differences resulted from different lighting conditions. Experiments for three kinds of classifiers (LVQ2, BP, and GPD) have been performed on a ITRI face database. GPD with the proposed light compensation approach displays the best recognition accuracy among all possible combination.

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Author information

Authors and Affiliations

  1. Computer & Communications Research Laboratories, Industrial Technology and Research Institute, Taiwan

    Yea-Shuan Huang & Yao-Hong Tsai

  2. Department of Electrical Engineering, YuanZe University, Taiwan

    Jun-Wei Shieh2

Authors
  1. Yea-Shuan Huang
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  2. Yao-Hong Tsai
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  3. Jun-Wei Shieh2
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Editor information

Editors and Affiliations

  1. Microsoft Research China, 5/F Beijing Sigma Center 49 Zhichung Road, Haidian District, Beijing, 100080, China

    Heung-Yeung Shum

  2. Institute of Information Science, Academia Sinica, Taiwan

    Mark Liao

  3. Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA

    Shih-Fu Chang

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© 2001 Springer-Verlag Berlin Heidelberg

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Huang, YS., Tsai, YH., Shieh2, JW. (2001). Robust Face Recognition with Light Compensation. In: Shum, HY., Liao, M., Chang, SF. (eds) Advances in Multimedia Information Processing — PCM 2001. PCM 2001. Lecture Notes in Computer Science, vol 2195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45453-5_31

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  • DOI: https://doi.org/10.1007/3-540-45453-5_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42680-6

  • Online ISBN: 978-3-540-45453-3

  • eBook Packages: Springer Book Archive

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