Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions

Author:

Dong Jun1234,Yuan Xue5,Xiong Fanlun1

Affiliation:

1. Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, P. R. China

2. School of Information Science and Engineering, Southeast University, Nanjing 210096, P. R. China

3. Wuxi Zhongke Intelligent Agricultural Development Co. Ltd., Wuxi 214000, P. R. China

4. Jiangsu R&D Center for Internet of Things, Wuxi 214000, P. R. China

5. School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing, P. R. China

Abstract

In this paper, a novel facial-patch based recognition framework is proposed to deal with the problem of face recognition (FR) on the serious illumination condition. First, a novel lighting equilibrium distribution maps (LEDM) for illumination normalization is proposed. In LEDM, an image is analyzed in logarithm domain with wavelet transform, and the approximation coefficients of the image are mapped according to a reference-illumination map in order to normalize the distribution of illumination energy due to different lighting effects. Meanwhile, the detail coefficients are enhanced to achieve detail information emphasis. The LEDM is obtained by blurring the distances between the test image and the reference illumination map in the logarithm domain, which may express the entire distribution of illumination variations. Then, a facial-patch based framework and a credit degree based facial patches synthesizing algorithm are proposed. Each normalized face images is divided into several stacked patches. And, all patches are individually classified, then each patch from the test image casts a vote toward the parent image classification. A novel credit degree map is established based on the LEDM, which is deciding a credit degree for each facial patch. The main idea of credit degree map construction is the over-and under-illuminated regions should be assigned lower credit degree than well-illuminated regions. Finally, results are obtained by the credit degree based facial patches synthesizing. The proposed method provides state-of-the-art performance on three data sets that are widely used for testing FR under different illumination conditions: Extended Yale-B, CAS-PEAL-R1, and CMUPIE. Experimental results show that our FR frame outperforms several existing illumination compensation methods.

Funder

Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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