计算机技术与发展2017，Vol.27Issue(8)：7-11,5.DOI:10.3969/j.issn.1673-629X.2017.08.002
基于同类测试样本组的稀疏表示人脸识别
Sparse Representation Classification for Face Recognition with Intra-class Testing-sample Group
摘要
Abstract
Recent studies have shown that Sparse Representation Classification (SRC) is an effective method for face recognition.SRC is a least squares classification based on l1-norm regularized for a single testing-sample.However,in the case that multiple testing-samples are known to be the same class which is surely helpful in the classification,the common-class label information is not included in SRC or other single-sample models.Therefore,a novel robust face recognition method based on sparse representation classification is proposed which is on the basis of IGSRC.Taking multiple intra-class testing-samples into the same group,it adopts the matrix L1-norm regularized least squares classification for sparse representation and judges the test sample group as the label with minimum error in classes.Experimental results show that compared with IRC and IGSRC,the method proposed cannot only obtain better face recognition rate (even when the number of training samples per subject is small or training samples are partly occluded),also own less running time.关键词
类内测试样本组/稀疏表示/人脸识别/矩阵L1-范数/多样本Key words
intra-class testing-samples/sparse representation/face recognition/matrix L1-norm/multiple samples分类
信息技术与安全科学引用本文复制引用
谢尚高,王丽平..基于同类测试样本组的稀疏表示人脸识别[J].计算机技术与发展,2017,27(8):7-11,5.基金项目
国家自然科学基金资助项目(11471159,61661136001) （11471159,61661136001）
南京航空航天大学研究生创新开放基金(kfjj20150706) （kfjj20150706）
