Makeup removal for face verification based upon contrastive learning

Jiawei Hou; Zhaohui Wang; Yigan Li

doi:10.1117/12.2599518

30 June 2021 Makeup removal for face verification based upon contrastive learning

Jiawei Hou, Zhaohui Wang, Yigan Li

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118781S (2021) https://doi.org/10.1117/12.2599518
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Makeup, derived from human`s pursuit of beauty, is widely accepted by the public. Despite its popularization, there are little effect been made to tackle the makeup face verification challenges. Aiming to promote existing verification system to accept or reject the claimed identity of a person with makeup in an image, a makeup robust face verification framework is proposed based upon a generative adversarial network. The proposal synthesizes non-makeup face images from makeup images for further verification. Specifically, a patchwise contrastive loss is introduced in the generative model to narrow the distance between makeup and non-makeup images. The challenge in the state-of-the-art is the employment of a pre-specified and hand-designed loss function to measure the performance, which is not the case in the proposal. Experimental results demonstrate that the proposal generates non-makeup faces with few artifacts and achieves 96.3% accuracy on Dataset1 in face verification, which is at least 0.8% better than some well discussed models.

Citation Download Citation

Jiawei Hou, Zhaohui Wang, and Yigan Li "Makeup removal for face verification based upon contrastive learning", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781S (30 June 2021); https://doi.org/10.1117/12.2599518

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