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Discriminative common vectors for face recognition

机译：识别人脸的识别通用向量

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摘要

In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, we propose a new face recognition method called the discriminative common vector method based on a variation of Fisher's linear discriminant analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's linear discriminant criterion given in the paper. Our test results show that the discriminative common vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.
机译：在人脸识别任务中，样本空间的大小通常大于训练集中样本的数量。结果，类内散布矩阵是奇异的，线性判别分析（LDA）方法无法直接应用。此问题称为“小样本量”问题。在本文中，我们基于小样本量情况下Fisher线性判别分析的变体，提出了一种新的人脸识别方法，称为判别共矢量方法。给出了两种不同的算法，以提取代表人脸数据库的训练集中每个人的可区分公共矢量。一种算法使用训练集中样本的类内散布矩阵，而另一种使用子空间方法和Gram-Schmidt正交化过程来获得可区分的公共矢量。然后，将可区分的公共向量用于新面孔的分类。所提出的方法为最大化本文给出的改进的Fisher线性判别准则提供了最优解。我们的测试结果表明，在识别精度，效率和数值稳定性方面，判别式通用矢量方法优于其他方法。

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