Simple illumination normalization algorithm for face recognition

Jaepil Ko, Eunju Kim, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


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 illuminationnormalized 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.

Original languageEnglish
Title of host publicationPRICAI 2002
Subtitle of host publicationTrends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsMitsuru Ishizuka, Abdul Sattar
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540440380, 9783540440383
Publication statusPublished - 2002
Event7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002 - Tokyo, Japan
Duration: 2002 Aug 182002 Aug 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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