Complete gradient face: a novel illumination invariant descriptor

Authors:
Jun-Yong Zhu;Wei-Shi Zheng;Jian-Huang Lai
Affiliations:
School of Mathematics and Computational Science, Sun Yat-sen University, China;School of Information Science and Technology, Sun Yat-Sen University, China;School of Information Science and Technology, Sun Yat-Sen University, China
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

In the past decade, illumination problem has been the bottleneck of developing robust face recognition systems. Extracting illumination invariant features, especially the gradient based descriptor [13], is an effective tool to solve this issue. In this paper, we propose a novel gradient based descriptor, namely Complete Gradient Face (CGF), to compensate the limitations in [13] and contribute in three folds: (1) we incorporate homogeneous filtering to alleviate the illumination effect and enhance facial information based on the Lambertian assumption; (2) we demonstrate the gradient magnitude in logarithm domain is insensitive to lighting change; (3) we propose a histogram based feature descriptor to integrate both magnitude and orientation information. Experiments on CMU-PIE and Extended YaleB are conducted to verify the effectiveness of our proposed method.