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IPFC: An Attentive Face Completion Network with Identity Preserving

Face completion is a challenging task since the facial textures and structures are hard to be captured and generated. Meanwhile, the blank area is usually completed by an average face, which may blur the individual information during the face completion processing. To solve these problems, we propose an Identity-Preserving Face Completion Network (IPFC). Firstly, we design an attentive generative module for achieving the pixel-level spatial relations and use the relations to capture the details of the face images. Then, we employ two discriminators to distinguish face authenticity and individual differences. During the optimization stage, we develop three schemes to minimize the generation error: (1) Full image and eye reconstruction losses are used to provide global and partial supervision, respectively. (2) The face authenticity loss is utilized as a basic restriction to keep the generated data distribution. (3) The individual discriminate loss is designed to capture the identity distribution from the training data without any other extra data. We apply our method on a popular benchmark CelebA-HQ, and it achieves state-of-the-art performances in four metrics.