An Unpaired Face Illumination Normalization Method Based on CycleGAN

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

doi:10.12052/gdutxb.180031

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An Unpaired Face Illumination Normalization Method Based on CycleGAN

更新时间：2026-05-27

- An Unpaired Face Illumination Normalization Method Based on CycleGAN
- Vol. 35, Issue 5, Pages: 11-19(2018)
- 作者机构：
  
  广东工业大学　计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.180031
  CLC： TP391
- Online First：10 July 2018，
  
  Published：2018
- 稿件说明：
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曾碧, 任万灵, 陈云华. 基于CycleGAN的非配对人脸图片光照归一化方法[J]. 广东工业大学学报, 2018, 35(5): 11-19.

Zeng Bi, Ren Wan-ling, Chen Yun-hua. An Unpaired Face Illumination Normalization Method Based on CycleGAN[J]. 2018, 35(5): 11-19.
曾碧, 任万灵, 陈云华. 基于CycleGAN的非配对人脸图片光照归一化方法[J]. 广东工业大学学报, 2018, 35(5): 11-19. DOI： 10.12052/gdutxb.180031.

Zeng Bi, Ren Wan-ling, Chen Yun-hua. An Unpaired Face Illumination Normalization Method Based on CycleGAN[J]. 2018, 35(5): 11-19. DOI： 10.12052/gdutxb.180031.

摘要

针对人脸识别过程中光照对识别结果的影响问题

提出了一种基于CycleGAN的光照归一化方法. 使用了生成对抗式的网络结构

利用图像翻译的原理

将较亮图片的光照风格迁移至较暗图片

同时保持原人脸表面平滑且结构基本不变. 使用非配对的数据集

无需人工标注标签

简化了数据准备阶段的工作

达到了利用无监督的深度学习方法去除图片光照影响的目的. 最后用训练好的模型处理CroppedYale测试集

比较处理前后的人脸识别准确率. 实验证明

本文方法具有较强的降低人脸光照对识别率影响的能力且基本不改变人脸结构

有利于提高人脸识别的准确率.

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.

关键词

Keywords

references

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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.

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.

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.

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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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