IEEE Trans Cybern. 2014 Oct;44(10):1950-61. doi: 10.1109/TCYB.2014.2300175.
The image of a face varies with the illumination, pose, and facial expression, thus we say that a single face image is of high uncertainty for representing the face. In this sense, a face image is just an observation and it should not be considered as the absolutely accurate representation of the face. As more face images from the same person provide more observations of the face, more face images may be useful for reducing the uncertainty of the representation of the face and improving the accuracy of face recognition. However, in a real world face recognition system, a subject usually has only a limited number of available face images and thus there is high uncertainty. In this paper, we attempt to improve the face recognition accuracy by reducing the uncertainty. First, we reduce the uncertainty of the face representation by synthesizing the virtual training samples. Then, we select useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples. Moreover, we state a theorem that determines the upper bound of the number of useful training samples. Finally, we devise a representation approach based on the selected useful training samples to perform face recognition. Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.
人脸的图像随光照、姿势和面部表情而变化，因此我们说，单个人脸图像在表示人脸方面具有高度的不确定性。从这个意义上说，人脸图像只是一种观察，不应该被认为是人脸的绝对准确表示。由于来自同一个人的更多人脸图像提供了更多关于人脸的观察，因此更多的人脸图像可能有助于降低人脸表示的不确定性并提高人脸识别的准确性。然而，在实际的人脸识别系统中，被试通常只有有限数量的可用人脸图像，因此存在高度的不确定性。在本文中，我们试图通过降低不确定性来提高人脸识别的准确性。首先，我们通过合成虚拟训练样本来降低人脸表示的不确定性。然后，我们从所有原始和合成的虚拟训练样本集合中选择与测试样本相似的有用训练样本。此外，我们还给出了一个确定有用训练样本数量上限的定理。最后，我们设计了一种基于所选有用训练样本的表示方法来进行人脸识别。在五个广泛使用的人脸数据库上的实验结果表明，我们提出的方法不仅可以获得很高的人脸识别准确率，而且计算复杂度也低于其他最先进的方法。
