[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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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]
基于深度学习特征的稀疏表示的人脸识别方法
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第3期
页码:
279-286
栏目:
学术论文—机器感知与模式识别
出版日期:
2016-06-25
- Title:
- Sparse representation via deep learning features based face recognition method
- Keywords:
- machine learning; biometric recognition; deep learning; feature learning; subspace; under-sampled recognition; sparse representation; face recognition
- 分类号:
- TP391.4
- 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
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、“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