Illumination normalization of face image based on illuminant direction estimation and improved Retinex

PLoS One. 2015 Apr 23;10(4):e0122200. doi: 10.1371/journal.pone.0122200. eCollection 2015.

Abstract

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biometric Identification / methods
  • Databases, Factual
  • Face / anatomy & histology*
  • Facial Expression
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / methods
  • Imaging, Three-Dimensional / methods
  • Light
  • Lighting / methods
  • Pattern Recognition, Automated / methods
  • Regression Analysis

Grants and funding

This work was supported in part by the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20121102130001), the China Postdoctoral Science Foundation (Grant No. 2013M540837), the Innovation Foundation of BUAA for PhD Graduates, and the National Natural Science Foundation of China (Grant No. 61103097). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.