Synthesizing for face recognition

Pose variance is one of the most challenging problem to 2D face recognition. In this paper, a novel frontal view face synthesizing strategy is introduced to improve the performance of traditional face recognition methods on non-frontal view input images. Given several non-frontal input faces, our minimum bending synthesizing strategy automatically picks up and merges information, to realize most natural frontal view face synthesizing. It is shown by experiments that our strategy could effectively reduce the influence of pose variance to face recognition, and rather than traditional landmark based approaches, our strategy does not require perfect landmark locating results.