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Filters: Author is Zhao, Fang  [Clear All Filters]
2017-03-07
Li, Jianshu, Zhao, Jian, Zhao, Fang, Liu, Hao, Li, Jing, Shen, Shengmei, Feng, Jiashi, Sim, Terence.  2016.  Robust Face Recognition with Deep Multi-View Representation Learning. Proceedings of the 2016 ACM on Multimedia Conference. :1068–1072.

This paper describes our proposed method targeting at the MSR Image Recognition Challenge MS-Celeb-1M. The challenge is to recognize one million celebrities from their face images captured in the real world. The challenge provides a large scale dataset crawled from the Web, which contains a large number of celebrities with many images for each subject. Given a new testing image, the challenge requires an identify for the image and the corresponding confidence score. To complete the challenge, we propose a two-stage approach consisting of data cleaning and multi-view deep representation learning. The data cleaning can effectively reduce the noise level of training data and thus improves the performance of deep learning based face recognition models. The multi-view representation learning enables the learned face representations to be more specific and discriminative. Thus the difficulties of recognizing faces out of a huge number of subjects are substantially relieved. Our proposed method achieves a coverage of 46.1% at 95% precision on the random set and a coverage of 33.0% at 95% precision on the hard set of this challenge.