A new face recognition method based on SVD perturbation for single example image per person
A new face recognition method based on SVD perturbation for single example image per person
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DOI：
10.1016/j.amc.2004.04.016
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发表时间：
2005-04
期刊：
影响因子：
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通讯作者：
Daoqiang Zhang;Songcan Chen;Zhi-Hua Zhou
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文献类型：
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作者：
Daoqiang Zhang;Songcan Chen;Zhi-Hua Zhou
目前，用于正视人脸识别的方法很多。然而，当每个类只有一个示例图像可用时，它们中几乎没有几个可以很好地工作。本文提出了一种基于奇异值分解摄动的处理“一例图像”问题的新方法，并提出了两种广义特征脸算法。在第一种算法中，将原始图像与通过扰动图像矩阵奇异值得到的衍生图像进行线性组合，然后对连接后的图像进行主成分分析(PCA)。在第二种算法中，将派生图像作为独立的图像来扩充训练图像集，然后对所有可用的训练图像进行PCA，包括原始图像和派生图像。在三种不同图像分辨率的FERET数据库上，将所提出的算法与标准特征脸算法和(PC)2A算法进行了比较。实验结果表明，广义特征脸算法比标准特征脸算法和(PC)2A算法具有更高的精度和更少的特征脸。
At present, there are many methods for frontal view face recognition. However, few of them can work well when only one example image per class is available. In this paper, we present a new method based on SVD perturbation to deal with the `one example image' problem and two generalized eigenface algorithms are proposed. In the first algorithm, the original image is linearly combined with its derived image gotten by perturbing the image matrix's singular values, and then principal component analysis (PCA) is performed on the joined images. In the second algorithm, the derived images are regarded as independent images that could augment training image set, and then PCA is performed on all the training images available, including the original ones and the derived ones. The proposed algorithms are compared with both the standard eigenface algorithm and the (PC)2A algorithm which is proposed for addressing the `one example image' problem, on the well-known FERET database with three different image resolutions. Experimental results show that the generalized eigenface algorithms are more accurate and use far fewer eigenfaces than both the standard eigenface algorithm and the (PC)2A algorithm.