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.
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Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Yin, W., Fu, Y., Sigal, L., Xue, X.: Semi-latent GAN: learning to generate and modify facial images from attributes. arXiv preprint arXiv:1704.02166
Bao, J., Chen, D., Wen, F., Li, H., Hua, G.: CVAE-GAN: fine-grained image generation through asymmetric training. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2745–2754 (2017)
Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1415–1424 (2017)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 53–58 (2002)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Demirel, H., Anbarjafari, G.: Pose invariant face recognition using probability distribution functions in different color channels. IEEE Sig. Process. Lett. 537–540 (2008)
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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Feng, Z., Xie, X., Lai, J., Huang, R. (2018). An Illumination Augmentation Approach for Robust Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_44
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DOI: https://doi.org/10.1007/978-3-319-97909-0_44
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