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
[1] ?陈旭, 张军, 陈文伟, 等. 卷积网络深度学习算法与实例[J]. 广东工业大学学报, 2017, 34(6):20-26 CHEN X, ZHANG J, CHEN W W, et al. Convolutional neural network algorithm and case[J]. Journal of Guangdong University of Technology, 2017, 34(6):20-26
[2] LEE P H, WU S W, HUNG Y P. Illumination compensation using oriented local histogram equalization and its application to face recognition[J]. IEEE Transactions on Image processing, 2012, 21(9):4280-4289
[3] CHEN W, ER M J, WU S. Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2006, 36(2):458-466
[4] 白小叶. 光照变化下的人脸识别算法研究[D]. 南京:南京邮电大学通信与信息工程学院, 2016.
[5] 聂祥飞, 杨志军, 何雪. 利用离散余弦变换与梯度脸的人脸光照处理[J]. 微型机与应用, 2017, 36(11):50-53 NIE X F, YANG Z J, HE X. Face illumination processing using discrete cosine transform and gradient faces[J]. Microcomputer & it's applications, 2017, 36(11):50-53
[6] GOODFELLOW I J, POUGET-ABADIE J, Mirza M, et al. Generative adversarial nets[C]//International Conference on Neural Information Processing Systems. Massachusetts:MIT Press, 2014:2672-2680.
[7] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.
[8] MIRZA M, OSINDERO S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.
[9] ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[J]. arXiv preprint arXiv:1703.10593, 2017.
[10] ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-image translation with conditional adversarial networks[J]. arXiv preprint arXiv:1611.07004, 2016.
[11] ULYANOV D, VEDALDI A, LEMPITSKY V. Instance normalization:The missing ingredient for fast stylization[J]. arXiv preprint arXiv:1607.08022, 2016.
[12] CLEVERT D A, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (elus)[J]. arXiv preprint arXiv:1511.07289, 2015.
[13] GAO W, CAO B, SHAN S G, et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2007, 38(1):149-161
[14] GEORGHIADES A S, BELHUMEUR P N, KRIEGMAN D J. From few to many:illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2001, 23(6):643-660
[15] LEE K C, HO J, KRIEGMAN D J. Acquiring linear subspaces for face recognition under variable lighting[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(5):684-98
[16] WU X, HE R, SUN Z N, et al. A light CNN for deep face representation with noisy labels[J]. arXiv preprint arXiv:1511.02683, 2015.
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