Using random subspace to combine multiple features for face recognition
Using random subspace to combine multiple features for face recognition
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DOI：
10.1109/afgr.2004.1301545
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发表时间：
2004-05
期刊：
影响因子：
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通讯作者：
Xiaogang Wang;Xiaoou Tang
中科院分区：
文献类型：
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作者：
Xiaogang Wang;Xiaoou Tang
LDA是一种流行的基于子空间的人脸识别方法。然而，它经常遭受小样本量的问题。在处理高维人脸数据时，由小样本训练集构造的LDA分类器往往存在偏差和不稳定性。在本文中，我们使用随机子空间方法（RSM），以克服小样本的LDA的问题。从面空间中随机生成一些低维子空间。从每个随机子空间构造LDA分类器，并且在最终决策中组合多个LDA分类器的输出。基于随机子空间LDA分类器，结合形状、纹理和Gabor小波响应，建立了一个鲁棒的人脸识别系统。该算法在XM2VTS数据库上取得了99.83%的准确率。
LDA is a popular subspace based face recognition approach. However, it often suffers from the small sample size problem. When dealing with the high dimensional face data, the LDA classifier constructed from the small training set is often biased and unstable. In this paper, we use the random subspace method (RSM) to overcome the small sample size problem for LDA. Some low dimensional subspaces are randomly generated from face space. A LDA classifier is constructed from each random subspace, and the outputs of multiple LDA classifiers are combined in the final decision. Based on the random subspace LDA classifiers, a robust face recognition system is developed integrating shape, texture, and Gabor wavelet responses. The algorithm achieves 99.83% accuracy on the XM2VTS database.