计算机科学 ›› 2015, Vol. 42 ›› Issue (8): 48-51.

• 2014’江苏省人工智能学术会议 • 上一篇    下一篇

改进的局部稀疏表示分类算法及其在人脸识别中的应用

尹贺峰,吴小俊,陈素根   

  1. 江南大学物联网工程学院 无锡214122,江南大学物联网工程学院 无锡214122,江南大学物联网工程学院 无锡214122;安庆师范学院数学与计算科学学院 安庆246133
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61373055)资助

Improved LSRC and its Application in Face Recognition

YIN He-feng, WU Xiao-jun and CHEN Su-gen   

  • Online:2018-11-14 Published:2018-11-14

摘要: 近年来,稀疏表示分类(Sparse Representation Based Classification,SRC)方法在人脸识别中受到越来越多的关注。原始SRC方法使用所有的训练样本组成字典矩阵,当训练样本比较多时,稀疏系数的求解会变得非常耗时。为了解决这一问题,提出一种新的局部稀疏表示分类(Local SRC,LSRC)方法。该方法针对每个测试样本,根据测试样本和训练样本稀疏系数之间的相似性来选择部分训练样本,由这些训练样本组成字典,然后在这个字典上对测试样本进行稀疏分解。该方法性能相比于原始LSRC方法更稳定。在ORL、Yale和AR人脸库上的实验结果表明,该方法的效果优于SRC和LSRC。

关键词: 稀疏表示分类,局部稀疏表示分类,稀疏系数,相似性,人脸识别

Abstract: Recently,sparse representation based classification(SRC) has attracted much attention in face recognition tasks.SRC forms the dictionary by directly using all the training samples.When giving lots of training samples,the speed of the subsequent sparse solver can be very slow.To alleviate this problem,a new local SRC,which is based on the similarities of sparse coefficients of both training samples and test samples,was presented.According to this similarity,a certain number of training samples are selected to form the over-complete dictionary,and then the test sample is decomposed using this dictionary.In contrast to original LSRC,which is based on kNN to choose neighbors of test samples,the proposed approach can steadily achieve better performance.Experimental results obtained on the ORL database,Yale database and AR database indicate that the proposed method is superior to both SRC and LSRC.

Key words: Sparse representation based classification(SRC),Local SRC(LSRC),Sparse coefficients,Similarity,Face recognition

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