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Makeup is widely used to improve facial attractiveness and is well acceptedby the public. However, different makeup styles will result in significantfacial appearance changes. It remains a challenging problem to match makeup andnon-makeup face images. This paper proposes a learning from generation approachfor makeup-invariant face verification by introducing a bi-level adversarialnetwork (BLAN). To alleviate the negative effects from makeup, we firstgenerate non-makeup images from makeup ones, and then use the synthesizednon-makeup images for further verification. Two adversarial networks in BLANare integrated in an end-to-end deep network, with the one on pixel level forreconstructing appealing facial images and the other on feature level forpreserving identity information. These two networks jointly reduce the sensinggap between makeup and non-makeup images. Moreover, we make the generator wellconstrained by incorporating multiple perceptual losses. Experimental resultson three benchmark makeup face datasets demonstrate that our method achievesstate-of-the-art verification accuracy across makeup status and can producephoto-realistic non-makeup face images.

Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification