  A standard pipeline of current face recognition frameworks consists of four
individual steps: locating a face with a rough bounding box and several
fiducial landmarks, aligning the face image using a pre-defined template,
extracting representations and comparing. Among them, face detection, landmark
detection and representation learning have long been studied and a lot of works
have been proposed. As an essential step with a significant impact on
recognition performance, the alignment step has attracted little attention. In
this paper, we first explore and highlight the effects of different alignment
templates on face recognition. Then, for the first time, we try to search for
the optimal template automatically. We construct a well-defined searching space
by decomposing the template searching into the crop size and vertical shift,
and propose an efficient method Face Alignment Policy Search (FAPS). Besides, a
well-designed benchmark is proposed to evaluate the searched policy.
Experiments on our proposed benchmark validate the effectiveness of our method
to improve face recognition performance.
