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A Simple Illumination Normalization Algorithm for Face Recognition

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PRICAI 2002: Trends in Artificial Intelligence (PRICAI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2417))

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Abstract

Most of the FR (face recognition) systems suffer from sensitivity to variations in illumination. For better performance the FR system needs more training samples acquired under variable lightings but it is not practical in real world. We introduce a novel pre-processing method, which makes illumination-normalized face image for face recognition. The proposed method, ICR (Illumination Compensation based on Multiple Regression Model), is to find the plane that best fits the intensity distribution of the face image using the multiple regression model, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experiments show a significant improvement of the recognition rate.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Yonsei University, 134, Shinchon-dong Sudaemoon-ku, Seoul, 120-749, Korea

    Jaepil Ko, Eunju Kim & Hyeran Byun

Authors
  1. Jaepil Ko
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  2. Eunju Kim
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  3. Hyeran Byun
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Editor information

Editors and Affiliations

  1. School of Information Science and Technology Department of Information and Communication Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Mitsuru Ishizuka

  2. School of Information Technology Knowledge Representation and Reasoning Unit (KRRU) Faculty of Engineering and Information Technology, Griffith University, PMB 50 Gold Coast Mail Centre, Queensland, 9726, Australia

    Abdul Sattar

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© 2002 Springer-Verlag Berlin Heidelberg

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Ko, J., Kim, E., Byun, H. (2002). A Simple Illumination Normalization Algorithm for Face Recognition. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_57

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  • DOI: https://doi.org/10.1007/3-540-45683-X_57

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44038-3

  • Online ISBN: 978-3-540-45683-4

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