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An SVD based Common Matrix Method for Face Recognition: Single Image per Person

机译:基于SVD的人脸识别通用矩阵方法:每人一张图像

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摘要

Common Matrix (CM) fails to work when there is only one image available in the training set. In this paper, an approach to solve this problem is proposed. By using singular value decomposition (SVD) null space of the image matrices are obtained. By projecting the image matrix onto the null space, common matrices are obtained for each class. After obtaining the common matrices, optimal projection vectors will be those that maximize the total scatter of the common matrices.
机译:当训练集中只有一个图像可用时,Common Matrix(CM)无法工作。本文提出了一种解决该问题的方法。通过使用奇异值分解(SVD),可以获得图像矩阵的零空间。通过将图像矩阵投影到零空间上,可以为每个类别获得通用矩阵。在获得公共矩阵之后,最佳投影向量将是那些使公共矩阵的总散度最大化的投影向量。

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