Joint Face Image Restoration and Frontalization for Recognition

• URL: http://arxiv.org/abs/2105.09907v1
• Date: Wed, 12 May 2021 03:52:41 GMT
• Title: Joint Face Image Restoration and Frontalization for Recognition
• Authors: Xiaoguang Tu, Jian Zhao, Qiankun Liu, Wenjie Ai, Guodong Guo, Zhifeng Li, Wei Liu, and Jiashi Feng
• Abstract summary: In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. Previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. We propose an Multi-Degradation Face Restoration model to restore frontalized high-quality faces from the given low-quality ones.
• Score: 79.78729632975744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.

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