Illumination normalization of face image

Shenggui Ling; Ye Lin; Keren Fu; Peng Cheng

doi:10.1117/12.2573135

12 June 2020 Illumination normalization of face image

Shenggui Ling, Ye Lin, Keren Fu, Peng Cheng

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191K (2020) https://doi.org/10.1117/12.2573135
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

A number of studies demonstrate that illumination is an important factor impacting the performance of computer vision tasks and illumination normalization can improve the performance of other visual analysis algorithms. At present, there are few methods aiming to illumination normalization of color face with deep learning. For this reason, we put forward a novel and practical deep fully convolutional neural network architecture for illumination normalization of color face. Comparing with the current methods based on deep learning, our approach does not need to input identity and illumination label. We preserve the identity by a well-designed generator and content loss. Experimental results show that the proposed method achieves favorable illumination normalization effect under various lighting variances and preserves identity effectively.

Citation Download Citation

Shenggui Ling, Ye Lin, Keren Fu, and Peng Cheng "Illumination normalization of face image", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191K (12 June 2020); https://doi.org/10.1117/12.2573135

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