An Illumination Augmentation Approach for Robust Face Recognition

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.