| 张石清,赵小明,楼宋江,闯跃龙,郭文平,陈盈.一种局部敏感的核稀疏表示分类算法[J].光电子激光,2014,(9):1812~1817 |
| 一种局部敏感的核稀疏表示分类算法 |
| A classification algorithm based on locality-sensitive kernel sparse representation for face recognition |
| 投稿时间：2014-04-10 |
| DOI： |
| 中文关键词: 核稀疏表示 数据局部性 人脸识别 |
| 英文关键词:kernel sparse representation data locality face recognition |
| 基金项目:国家自然科学基金(61272261、61203257)资助项目 (台州学院 图像处理与模式识别研究所,浙江 台州 318000) |
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| 中文摘要: |
| 为了克服核稀疏表示分类(KSRC)算法无法获取数 据的局部性信息从而导致获取的稀疏表示系数判别性受到限制的不足,提出一种局部敏感的 KSRC(LS-KSRC)算法用于人脸识别。通过在核特征空间中同时集成稀疏性和数据局部 性信息,从而获取具有良好判 别性的用于分类的稀疏表示系数。在标准的ORL人脸数据库和Extended Yale B人脸 数据库的试验结果表明, 本文方法的分类性能优于传统的(KSRC)算法、稀疏表示分类(SRC)算法、局部线性约束 编 码(LLC)、支持向量机(SVM)、最近邻法(NN)以及最近邻子空间法(NS),用于人脸 识别能够取得优越的分类性能。 |
| 英文摘要: |
| Kernel sparse representation-based classification (KSRC) is currently one of hot research topics in the fields of pattern recognition and computer vision KSRC has excellent cla ssification performance,but is not able to obtain the important information about data locality,resulting in the f act that the discriminating power of sparse representation coefficients yielded by KSRC is reduced.Considering the i mportance of data locality,in this paper a new classification algorithm based on locality-sensitive kernel sparse representation is proposed for face recognition.The proposed method integrates both sparsity and data locality in t he kernel feature space so that it can obtain good discriminating sparse representation coefficients for classifica tion.Experimental results on two benchmarking face databases,i.e.,the ORL database and the Extended Yale B data base,demonstrate that the proposed method outperforms kernel sparse representation-based classification ( KSRC),sparse representation-based classification (SRC),locality-constrained linear coding (LLC),support vector machines (SVM),the nearest neighbor (NN),and the nearest subspace (NS).Therefore,the proposed method is able to achieve promising performance for face recognition. |
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