High Performance Large Scale Face Recognition with Multi-cognition Softmax and Feature Retrieval

In this paper, we introduce our solution to the Challenge-1 of the MS-Celeb-lM challenges which aims to recognize one million celebrities. To solve this large scale face recognition problem, a Multi-Cognition Softmax Model (MCSM) is proposed to distribute training data to several cognition units by a data shuffling strategy. Here we introduce one cognition unit as a group of independent softmax models, which is designed to increase the diversity of the one softmax model to boost the performance for models ensemble. Meanwhile, a template-based Feature Retrieval (FR) module is adopted to improve the performance of MCSM by a specific voting scheme. Moreover, a one-shot learning method is applied on collected extra 600K identities due to each identity has one image only. Finally, testing images with lower score from MCSM and FR are assigned new labels with higher score by merging one-shot learning results. Extensive experiments on the MS-Celeb-1M testing set demonstrate the superiority of the proposed method. Our solution ranks the first place in both two settings of the final evaluation and outperforms other teams by a large margin.