基于自步学习的加权稀疏表示人脸识别方法

doi:10.11772/j.issn.1001-9081.2017.11.3145

计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3145-3151.DOI: 10.11772/j.issn.1001-9081.2017.11.3145

• 第十六届中国机器学习会议(CCML 2017) • 上一篇下一篇

基于自步学习的加权稀疏表示人脸识别方法

王学军¹, 王文剑^1,2, 曹飞龙³

1. 山西大学计算机与信息技术学院, 太原 030006;
2. 计算智能与中文信息处理教育部重点实验室, 太原 030006;
3. 中国计量大学理学院, 杭州 310018

收稿日期:2017-05-16 修回日期:2017-07-05 出版日期:2017-11-10 发布日期:2017-11-11
通讯作者: 王文剑
作者简介:王学军(1989-),男,河北邢台人,博士研究生,主要研究方向:机器学习、图像处理;王文剑(1968-),女,山西太原人,教授,博士,CCF高级会员,主要研究方向:计算智能、数据挖掘;曹飞龙(1965-),男,浙江仙居人,教授,博士,主要研究方向:神经网络、计算智能。
基金资助:
国家自然基金资助项目（61673249，61672477，61503229）；山西省回国留学人员科研资助项目（2016-004）。

Weighted sparse representation based on self-paced learning for face recognition

WANG Xuejun¹, WANG Wenjian^1,2, CAO Feilong³

1. College of Computer and Information Technology, Shanxi University, Taiyuan Shanxi 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan Shanxi 030006, China;
3. College of Science, China Jiliang University, Hangzhou Zhejiang 310018, China

Received:2017-05-16 Revised:2017-07-05 Online:2017-11-10 Published:2017-11-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61673249, 61672477, 61503229), the Research Project Supported by Shanxi Scholarship Council of China (2016-004).

摘要/Abstract

摘要： 近年来基于稀疏表示的分类方法（SRC）成为了一个新的热点问题，在人脸识别领域取得了很大的成功。但基于稀疏表示的方法在重建待测样本时，有可能会利用与待测样本相差较大的训练样本，并且没有考虑到表示系数的局部信息，从而导致分类结果不稳定。提出一种基于自步学习的加权稀疏表示算法SPL-WSRC，在字典中有效剔除与待测样本相差较大的训练样本，并利用加权手段考虑样本间的局部信息，以提高分类精度和稳定性。通过3个典型的人脸数据集中的实验，实验结果表明，所提算法优于原稀疏表示算法SRC，特别是当训练样本足够多时，效果更明显。

关键词: 基于稀疏表示的分类方法, 分类, 自步学习, 加权系数, 人脸识别

Abstract: In recent years, Sparse Representation based Classifier (SRC) has become a hot issue which has been great successful in face recognition. However, when the SRC reconstructed test samples, it is possible to use the training samples with large difference from the test samples, meanwhile, SRC tends to lose locality information and thus produces unstable classification results. A Self-Paced Learning Weighted Sparse Representation based Classifier (SPL-WSRC) was proposed. It could effectively eliminate the training samples with large difference from the test samples. In addition, locality information between the samples was considered by weighting to improve the classification accuracy and stability. The experimental results on three classical face databases show that the proposed SPL-WSRC algorithm is better than the original SRC algorithm. The effect is more obvious, especially when the training samples are enough.

Key words: Sparse Representation based Classifier (SRC), classification, self-paced learning, weighting coefficient, face recognition

中图分类号:

TP391.41

王学军, 王文剑, 曹飞龙. 基于自步学习的加权稀疏表示人脸识别方法[J]. 计算机应用, 2017, 37(11): 3145-3151.

WANG Xuejun, WANG Wenjian, CAO Feilong. Weighted sparse representation based on self-paced learning for face recognition[J]. Journal of Computer Applications, 2017, 37(11): 3145-3151.

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基于自步学习的加权稀疏表示人脸识别方法

Weighted sparse representation based on self-paced learning for face recognition

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