  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.
