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Attention-Set based Metric Learning for Video Face Recognition
Yibo Hu1,2,3; Xiang Wu1,2; Ran He1,2,3
2017-11
会议名称Asian Conference on Pattern Recognition (ACPR)
会议日期2017.11.26-2017.11.29
会议地点Nanjing, China
摘要
Face recognition has made great progress with the development of deep learning. However, video face recognition (VFR) is still an ongoing task due to various illumination, low-resolution, pose variations and motion blur. In this paper, we propose a novel Attention-Set based Metric
Learning (ASML) method for VFR. It is a promising and generalized extension of Maximum Mean Discrepancy with Memory Attention Weighting inspired by Neural Turing Machine. ASML can be naturally integrated into Convolutional Neural Networks, resulting in an end-to-end learning scheme. Our method achieves state-of-the-art performance for the task of video face recognition on three widely used benchmarks including YouTubeFace, YouTube Celebrities and Celebrity-1000.
关键词Video Face Recognition Metric Learning Memory Attention Weighting
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19626
专题智能感知与计算研究中心
作者单位1.Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Yibo Hu,Xiang Wu,Ran He. Attention-Set based Metric Learning for Video Face Recognition[C],2017.
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