Face Recognition via Active Annotation and Learning

Hao Ye1, Weiyuan Shao1, Hong Wang1, Jianqi Ma2, Li Wang2, Yingbin Zheng1, Xiangyang Xue2

1Shanghai Advanced Research Institute, CAS      2School of Computer Science, Fudan University

[See more details in our MM'16 paper]


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.

Figure: Our face annotation and recognition framework.


Active annotations of CASIA-WebFace Database (445,327 images) Google Drive / BaiduYun.

Please cite our paper if you use the annotaion in your research:

Hao Ye, Weiyuan Shao, Hong Wang, Jianqi Ma, Li Wang, Yingbin Zheng, Xiangyang Xue, Face Recognition via Active Annotation and Learning, ACM Multimedia Conference (MM), 2016. [pdf][bibtex]

The original face images can be downloaded from the CASIA-WebFace website [1].


[1] D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014.