用于人脸识别中平衡对齐技术的联合学习方法（CS CV）---Elva

人脸对齐是人脸识别的关键技术，已被广泛采用。然而，目前的实践过于简单，探索不足。人们缺乏对人脸对齐的重要性以及如何进行人脸对齐的理解。本文对这些问题进行了研究，并对其做出了两方面的贡献。首先，我们对对准强度如何影响识别精度进行了深入和定量的研究，结果表明，过度对准是有害的，需要找到一个最优的平衡基准点。为了达到平衡，我们的第二个贡献是一种新的联合学习方法，其中对齐学习的强度是可控的，由具体的识别而定。通过对多个基准点的综合实验，特别是对大角度的有挑战性的基准点的综合实验，验证了本方法的有效性。

原文题目：Balanced Alignment for Face Recognition: A Joint Learning Approach

原文：Face alignment is crucial for face recognition and has been widely adopted. However, current practice is too simple and under-explored. There lacks an understanding of how important face alignment is and how it should be performed, for recognition. This work studies these problems and makes two contributions. First, it provides an in-depth and quantitative study of how alignment strength affects recognition accuracy. Our results show that excessive alignment is harmful and an optimal balanced point of alignment is in need. To strike the balance, our second contribution is a novel joint learning approach where alignment learning is controllable with respect to its strength and driven by recognition. Our proposed method is validated by comprehensive experiments on several benchmarks, especially the challenging ones with large pose.

原文作者：Huawei Wei, Peng Lu, Yichen Wei

原文链接：https://arxiv.org/abs/2003.10168