Learning Discriminative and Complementary Patches for Face Recognition

The ensemble of convolutional neural networks (CNNs) has widely been used in many computer vision tasks including face recognition. Many existing ensembles of face recognition CNNs apply a two-stage pipeline to target performance improvement [10], [20], [22], [23], [29]: (1) it trains multiple CNNs separately with many face patches covering different facial areas; (2) the features derived from different models are aggregated off-line by different fusion methods. The well-known face recognition work, DeepID2 [20] trains 200 networks based on 200 arbitrarily chosen facial areas and chooses the best 25 ones to achieve impressive performance. However, it is very time-consuming to train so many networks. In addition, a brute-force like way of choosing facial patches is used without knowing which face patches are complementary and discriminative. It might be lack of generalization capability for cross-database applications. To solve that, we propose a novel end-to-end CNN ensemble architecture which automatically learns the complementary and discriminative patches for face recognition. Specifically, we propose a novel Patch Generation Engine (PGE) with Patch Search Spatial Transformer Network (PS-STN) and ROI shrunk loss to perform the patch selection process. ROI shrunk loss enlarges the distance of learned features in spatial space and feature space and learn complementary features. In order to get final aggregated feature, we use a supervised fusion module named Two Stage Discriminative Fusion Module (TSDFM) which effective to capture the global and local information and further guide the PGE to learn better patches. Extensive experiments conducted on LFW and YTF datasets show the effectiveness of our novel end-to-end ensemble method.