[1]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报编辑部,2016,11(3):279-286.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11(3):279-286.[doi:10.11992/tis.201603026]
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基于深度学习特征的稀疏表示的人脸识别方法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年3期
页码:
279-286
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
Sparse representation via deep learning features based face recognition method
作者:
马晓12 张番栋12 封举富12
1. 北京大学 信息科学技术学院, 北京 100871;
2. 北京大学 机器感知与智能教育部重点实验室, 北京 100871
Author(s):
MA Xiao12 ZHANG Fandong12 FENG Jufu12
1. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
2. Key Laboratory of Machine Perception (Ministry of Education) Department of Machine Intelligence, Peking University, Beijing 100871, China
关键词:
机器学习生物特征识别深度学习特征学习子空间小样本稀疏表示人脸识别
Keywords:
machine learningbiometric recognitiondeep learningfeature learningsubspaceunder-sampled recognitionsparse representationface recognition
分类号:
TP391.4
DOI:
10.11992/tis.201603026
摘要:
本文针对传统的基于稀疏表示的人脸识别方法在小样本情况下对类内变化鲁棒性不强的问题,从特征的层面入手,提出了基于深度学习特征的稀疏表示的人脸识别方法。本方法首先利用深度卷积神经网络提取对类内变化不敏感的人脸特征,然后通过稀疏表示对所得人脸特征进行表达分类。本文通过实验,说明了深度学习得到的特征也具有一定的子空间特性,符合基于稀疏表示的人脸识别方法对于子空间的假设条件。实验证明,基于深度学习特征的稀疏表示的人脸识别方法具有较好的识别准确度,对类内变化具有很好的鲁棒性,特别在小样本问题中具有尤为突出的优势。
Abstract:
Focusing on the problems that the traditional sparse representation based face recognition methods are not quite robust to intra-class variations, a novel Sparse Representation via Deep Learning Features based Classification (SRDLFC) method is proposed in this paper, employing a deep convolutional neural network to extract facial features and a sparse representation based framework to make classification. Experimental results in this paper also verifies the features extracted from deep convolutional network do satisfy the linear subspace assumption. The proposed SRDLFC proves to be quite effective and be robust to intra-class variations especially for under-sampled face recognition problems.

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备注/Memo

备注/Memo:
收稿日期:2016-3-16;改回日期:。
基金项目:国家自然科学基金项目(61333015);国家重点基础研究发展计划(2011CB302400).
作者简介:马晓,男,1990年生,博士研究生,主要研究方向为机器学习、模式识别和子空间理论。张番栋,男,1991年生,博士研究生,主要研究方向为机器学习和生物特征识别。封举富,男,1967年生,教授,博士生导师,博士,主要研究方向为图像处理、模式识别、机器学习和生物特征识别。主持参与国家自然科学基金、教育部新世纪优秀人才支持计划、“十一五”国家科技支撑计划课题、973计划等多项项目。曾获中国高校科技二等奖等多项奖励。
通讯作者:马晓.E-mail:maxiao2012@pku.edu.cn.
更新日期/Last Update: 1900-01-01