Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform

  • Authors:

  • Xiaohua Xie;Jianhuang Lai;Wei-Shi Zheng

  • Affiliations:

  • School of Mathematics & Computational Science, Sun Yat-sen University, China and Guangdong Province Key Laboratory of Information Security, China;School of Information Science and Technology, Sun Yat-sen University, China and Guangdong Province Key Laboratory of Information Security, China;Department of Computer Science, Queen Mary University of London, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

Face recognition under varying lighting conditions is challenging, especially for single image based recognition system. Exacting illumination invariant features is an effective approach to solve this problem. However, existing methods are hard to extract both multi-scale and multi-directivity geometrical structures at the same time, which is important for capturing the intrinsic features of a face image. In this paper, we propose to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform. LNSCT can extract strong edges, weak edges, and noise from a face image using NSCT in the logarithm domain. We analyze that in the logarithm domain the low-pass subband of a face image and the low frequency part of strong edges can be regarded as the illumination effects, while the weak edges and the high frequency part of strong edges can be considered as the reflectance component. Moreover, even though a face image is polluted by noise (in particular the multiplicative noise), the reflectance component can still be well estimated and meanwhile the noise is removed. The LNSCT can be applied flexibly as neither assumption on lighting condition nor information about 3D shape is required. Experimental results show the promising performance of LNSCT for face recognition on Extended Yale B and CMU-PIE databases.