计算机科学 ›› 2010, Vol. 37 ›› Issue (9): 267-269.

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基于稀疏表示的人脸识别方法

杨荣根,任明武,杨静宇   

  1. (南京理工大学计算机学院 南京210094);(淮阴工学院计算机工程系 淮安223003)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(60875010)资助。

Sparse Representation Based Face Recognition Algorithm

YANG Rong-gen,REN Ming-wu,YANG Jing-yu   

  • Online:2018-12-01 Published:2018-12-01

摘要: 分析了稀疏表示的数学本质就是稀疏正规化约束下的信号分解,研究了一种正交匹配追踪的稀疏表示算法并利用矩阵Cholcsky分解简化迭代过程中矩阵求逆计算来快速实现算法,将该算法应用在人脸识别中,利用训练样本构建冗余字典,将测试样本看成冗余字典中训练样本的线性组合,通过在不同人脸库上的实验证明了该方法的有效性。

关键词: 稀疏表示,稀疏编码,压缩感知,正交匹配追踪,特征提取

Abstract: We analyzed the mathematic essence of sparse representation, sparse regularized signal decomposition. Studied a sparse representation algorithm of orthogonal matching pursuit Using the matrix Cholesky decomposition,we realined the OMP algorithm a fast version. We cast the recognition problem as one of classifying among multiple linear regression models and developed a new framework from sparse signal representation. We viewed a test sample as the linear combination of training samples. We conducted experiments on face recognition to verify the efficacy of the proposed algorithm.

Key words: Sparse representation, Sparse coding, Compressed sensing, Orthogonal matching pursuit, Feature extraction

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