Attention-Set based Metric Learning for Video Face Recognition | |
Yibo Hu1,2,3![]() ![]() ![]() | |
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 | 模式识别实验室 |
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 |
第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
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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PID4983875.pdf(496KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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