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Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

机译：使用双稀疏局部Fisher判别分析的人脸识别

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摘要

Local Fisher discriminant analysis (LFDA) was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP) for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA) for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA) for face recognition. The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint. Then, DSLFDA reformulates the objective function as a regression-type optimization problem. The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to a l(1) penalty. Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.
机译：提出了用于处理多峰问题的本地Fisher判别分析（LFDA）。它不仅结合了用于保存高维数据局部结构的局部保留投影（LPP）的思想，而且还结合了用于获得判别力的Fisher判别分析（FDA）的思想。但是，LFDA还存在采样不足的问题以及许多降维方法。同时，投影矩阵不稀疏。在本文中，我们提出了用于人脸识别的双稀疏局部Fisher判别分析（DSLFDA）。该方法首先构造了一个具有非负约束的稀疏数据自适应图。然后，DSLFDA将目标函数重新表述为回归型优化问题。自然避免了欠采样问题，并且可以通过将回归类型问题添加到l（1）惩罚中来获得稀疏解。在Yale，ORL和CMU PIE人脸数据库上进行了实验，以证明该方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|636928.1-636928.9|共9页
  • 作者

    Wang Zhan; Ruan Qiuqi; An Gaoyun;

  • 作者单位

    Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China.;

    Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China.;

    Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China.;

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