Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (05): 11-19.doi: 10.12052/gdutxb.180031

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An Unpaired Face Illumination Normalization Method Based on CycleGAN

Zeng Bi, Ren Wan-ling, Chen Yun-hua   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2018-03-08 Online:2018-07-10 Published:2018-07-10

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Abstract: Aiming at the influence of illumination in face recognition process, a method of illumination normalization based on CycleGAN is presented. By using the Generative Adversarial Nets and the principle of image translation, the illumination style of the darker image was shifted to the brighter image, while the surface and the structure of the face kept smooth at the same time. Unpaired data sets without labels are used, in order to achieve unsupervised removal of illumination, and greatly simplify the work of data preprocessing. Finally, the CroppedYale data set is used to train a deep learning face recognition model, using this CycleGAN model to process the test set, and comparing the accuracy before and after processing. Experiments show that this method has a strong ability to reduce the influence of human face illumination on recognition rate while basically not changing the face structure, and therefore is helpful to improve the accuracy of face recognition.

Key words: generative adversarial nets, deep learning, face recognition, illumination normalization, face illumination processing

CLC Number: 

  • TP391
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