论文标题
面部识别的平衡对齐：一种联合学习方法
Balanced Alignment for Face Recognition: A Joint Learning Approach
论文作者
论文摘要
面部对齐对于面部识别至关重要，并且已被广泛采用。但是，当前的练习太简单且探索不足。缺乏对面部对齐方式的重要性以及应如何执行的理解。这项工作研究了这些问题，并做出了两种贡献。首先，它提供了一项深入和定量的研究，该研究对一致性强度如何影响识别精度。我们的结果表明，过度对齐是有害的，并且需要一个最佳的平衡点。为了达到平衡，我们的第二个贡献是一种新颖的联合学习方法，在该方法中，对齐学习的力量可以控制并受识别的驱动。我们提出的方法通过几个基准测试的全面实验，尤其是具有较大姿势的具有挑战性的方法来验证。
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.
