Face Recognition via Active Annotation and Learning

Hao Ye, Weiyuan Shao, Hong Wang, Jianqi Ma, Li Wang, Yingbin Zheng, Xiangyang Xue

Abstract: In this paper, we introduce an active annotation and learning framework for the face recognition task. Starting with an initial label deficient face image training set, we iteratively train a deep neural network and use this model to choose the examples for further manual annotation. We follow the active learning strategy and derive the Value of Information criterion to actively select candidate annotation images. During these iterations, the deep neural network is incrementally updated. Experimental results conducted on LFW benchmark and MS-Celeb-1M challenge demonstrate the effectiveness of our proposed framework.