Efficient Decomposition Convolution and Temporal Pyramid Network for Video Face Recognition

被引：0
|
作者：
Zhou S.-T. [1 ]
Yan X. [1 ]
Xie Z.-S. [1 ]
机构：
[1] Glasgow Collge, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 02期
关键词：
Convolutional neural network; Decomposition convolution; Face recognition; Temporal pyramid network; Video analysis;
D O I：
10.12178/1001-0548.2020319
中图分类号：
学科分类号：
摘要：
With a large number of video surveillance and camera networks, face recognition of continuous video frames in unrestricted scenes is becoming more and more attractive. Most of the traditional face recognition methods for continuous video frames have the problem of fluctuating recognition results and intensive computing resources. In this paper, an efficient 3D decomposition convolution is designed, which can effectively reduce the computational consumption of video face recognition and improve the recognition accuracy. Finally, we also propose a temporal pyramid network to further effectively mine complementary information between frames to improve the recognition accuracy. The performance has been tested on YTF and PaSC datasets. Copyright ©2021 Journal of University of Electronic Science and Technology of China. All rights reserved.
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收藏
页码：231 / 235
页数：4
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