GEOMETRY-AWARE METRIC LEARNING FOR SIMILAR FACE RECOGNITION

Noticing that face images(from different persons) with high similarity computed by current state-of-the-art methods may be not visually similar, in this paper, we present a new verification problem on judging whether the given faces are similar or not. Similar to "view 2" of Labeled Faces in the Wild(LFW), we construct ten subsets' face pairs using images from LFW. Label of each pair comes from human annotation results. Since similar faces are not from the same person after all, pushing similar faces too close will easily contribute to wrong models. Therefore, we propose a new geometry-aware metric learning(GAML) method which can preserve the similarity of similar faces while enlarge the difference between dissimilar faces. Experimental results show that our method outperforms traditional face verification methods on our similar face dataset.