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基于字典学习的核稀疏表示人脸识别方法
引用本文:朱杰,杨万扣,唐振民.基于字典学习的核稀疏表示人脸识别方法[J].模式识别与人工智能,2012,25(5):859-864.
作者姓名:朱杰  杨万扣  唐振民
作者单位:1。南京理工大学计算机科学与技术学院南京210094
2。南京晓庄学院数学与信息技术学院南京211171
3。东南大学自动化学院南京210018
基金项目:国家自然科学基金资助项目
摘    要:受Metafaces方法的启发,提出一种基于字典学习方法的核稀疏表示方法并成功应用于人脸识别。首先,采用核技术将稀疏表示方法推广到高维空间得到核稀疏表示方法。其次,借鉴Metaface字典学习方法,进行字典学习得到一组核基向量构成核稀疏表示字典。最后,利用学习得到的核字典基重构样本,并根据样本与重构样本之间的残差最小原则对人脸图像进行分类。在AR、ORL和Yale人脸数据库上的实验表明该方法的良好识别性能。

关 键 词:Metaface学习  核技术  稀疏表示  人脸识别  
收稿时间:2011-04-11

A Dictionary Learning Based Kernel Sparse Representation Method for Face Recognition
ZHU Jie , YANG Wan-Kou , TANG Zhen-Min.A Dictionary Learning Based Kernel Sparse Representation Method for Face Recognition[J].Pattern Recognition and Artificial Intelligence,2012,25(5):859-864.
Authors:ZHU Jie  YANG Wan-Kou  TANG Zhen-Min
Affiliation:1. School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094
2.School of Mathematics and Information Technology,Nanjing Xiaozhuang University,Nanjing 211171
3.School of Automation,Southeast University,Nanjing 210018
Abstract:Inspired by Metafaces, a dictionary learning based kernel sparse representation method for face recognition is presented. Firstly, a kernel sparse representation classifier is proposed by extending sparse representation classifier to high dimensional space via kernel functions. Then, the kernel dictionary bases are learned based on Metafaces framework. Finally, the samples are reconstructed by kernel dictionary and the face images are classified according to the residual. The experimental results on AR, ORL and Yale face databases show that the proposed method works well.
Keywords:Metaface Learning  Kernel Technique  Sparse Representation  Face Recognition  
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