In the field of face recognition, a key issue is whether there are a sufficient number of face training samples with valid information. Due to the complexity of human face images, face recognition is easy to be affected by the external environment such as light intensity, gesture expression, hairstyle, and occlusion. Therefore, it is difficult to obtain enough effective samples in practical applications. In this paper, we propose a new algorithm that generates virtual images by utilizing the information of the test sample via singular value decomposition. The virtual images not only extend the training sample set but also can better adapt to the test sample. In addition, we use the weighted score fusion scheme to calculate the ultimate result, which can better take advantages of data from different sources including original images and virtual images. Experimental results on the Extended Yale_B, AR, GT, ORL, and FERET face databases prove that our algorithm can obtain satisfactory performance.

中文翻译：

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基于奇异值分解的虚拟人脸识别

在面部识别领域，一个关键问题是是否有足够数量的带有有效信息的面部训练样本。由于人脸图像的复杂性，人脸识别容易受到诸如光强度，手势表达，发型和遮挡等外部环境的影响。因此，在实际应用中很难获得足够的有效样品。在本文中，我们提出了一种新算法，该算法通过奇异值分解利用测试样本的信息来生成虚拟图像。虚拟图像不仅扩展了训练样本集，而且可以更好地适应测试样本。此外，我们使用加权分数融合方案来计算最终结果，这样可以更好地利用来自不同来源的数据，包括原始图像和虚拟图像。在扩展Yale_B，AR，GT，ORL和FERET人脸数据库上的实验结果证明，我们的算法可以获得令人满意的性能。