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基于鉴别稀疏保持嵌入的人脸识别算法

马小虎 谭延琪

马小虎, 谭延琪. 基于鉴别稀疏保持嵌入的人脸识别算法. 自动化学报, 2014, 40(1): 73-82. doi: 10.3724/SP.J.1004.2014.00073
引用本文: 马小虎, 谭延琪. 基于鉴别稀疏保持嵌入的人脸识别算法. 自动化学报, 2014, 40(1): 73-82. doi: 10.3724/SP.J.1004.2014.00073
MA Xiao-Hu, TAN Yan-Qi. Face Recognition Based on Discriminant Sparsity Preserving Embedding. ACTA AUTOMATICA SINICA, 2014, 40(1): 73-82. doi: 10.3724/SP.J.1004.2014.00073
Citation: MA Xiao-Hu, TAN Yan-Qi. Face Recognition Based on Discriminant Sparsity Preserving Embedding. ACTA AUTOMATICA SINICA, 2014, 40(1): 73-82. doi: 10.3724/SP.J.1004.2014.00073

基于鉴别稀疏保持嵌入的人脸识别算法

doi: 10.3724/SP.J.1004.2014.00073
基金项目: 

国家自然科学基金(61272258)资助

详细信息
    作者简介:

    马小虎 苏州大学计算机科学与技术学院教授. 1997 年获浙江大学博士学位.主要研究方向为图像处理,模式识别,计算机图形学. 本文通信作者.E-mail:xhma@suda.edu.cn

Face Recognition Based on Discriminant Sparsity Preserving Embedding

Funds: 

Supported by National Natural Science Foundation of China (61272258)

  • 摘要: 鉴于近年来稀疏表示(Sparse representation,SR)在高维数据例如人脸图像的特征提取与降维领域的快速发展,对原始的稀疏保持投影(Sparsity preserving projection,SPP)算法进行了改进,提出了一种叫做鉴别稀疏保持嵌入(Discriminant sparsity preserving embedding,DSPE)的算法. 通过求解一个最小二乘问题来更新SPP中的稀疏权重并得到一个更能真实反映鉴别信息的鉴别稀疏权重,最后以最优保持这个稀疏权重关系为目标来计算高维数据的低维特征子空间.该算法是一个线性的监督学习算法,通过引入鉴别信息,能够有效地对高维数据进行降维. 在ORL库、Yale库、扩展Yale B库和CMU PIE库上的大量实验结果验证了算法的有效性.
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出版历程
  • 收稿日期:  2012-07-28
  • 修回日期:  2012-12-20
  • 刊出日期:  2014-01-20

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