Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification Yi Li, Lingxiao Song, Xiang Wu, Ran He∗, and Tieniu Tan National Laboratory of Pattern Recognition, CASIA Center for Research on Intelligent Perception and Computing, CASIA Center for Excellence in Brain Science and Intelligence Technology, CAS University of Chinese Academy of Sciences, Beijing 100190, China
[email protected], flingxiao.song, rhe,
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[email protected] Abstract Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introduc- ing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non- makeup images for further verification. Two adversarial net- works in BLAN are integrated in an end-to-end deep net- work, with the one on pixel level for reconstructing appeal- ing facial images and the other on feature level for preserv- ing identity information. These two networks jointly reduce Figure 1: Samples of facial images with (the first and the the sensing gap between makeup and non-makeup images. third columns) and without (the second and the fourth Moreover, we make the generator well constrained by incor- columns) the application of cosmetics. The significant dis- porating multiple perceptual losses. Experimental results on crepancy of the same identity can be observed.