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An Illumination Augmentation Approach for Robust Face Recognition

  • Zhanxiang Feng
  • Xiaohua Xie
  • Jianhuang Lai
  • Rui Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Deep learning has achieved great success in face recognition and significantly improved the performance of the existing face recognition systems. However, the performance of deep network-based methods degrades dramatically when the training data is insufficient to cover the intra-class variations, e.g., illumination. To solve this problem, we propose an illumination augmentation approach to augment the training set by constructing new training images with additional illumination components. The proposed approach first utilizes an external benchmark to generate several illumination templates. Then we combine the generated templates with the training images to simulate different illumination conditions. Finally, we conduct color correction by using the singular value decomposition (SVD) algorithm to confirm that the color of the augmented image is consistent with the input image. Experimental results demonstrate that the proposed illumination augmentation approach is effective for improving the performance of the existing deep networks.

Keywords

Face recognition Deep learning Illumination augmentation 

Notes

Acknowledgments

This project was supported by the NSFC (U1611461, 61573387, 61672544) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (NO. 2016TQ03X263).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhanxiang Feng
    • 1
  • Xiaohua Xie
    • 2
    • 3
  • Jianhuang Lai
    • 2
    • 3
  • Rui Huang
    • 2
  1. 1.School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  3. 3.Guangdong Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of EducationGuangzhouChina

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