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Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person

机译:每人单个训练样本的识别性多歧管分析

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Conventional appearance-based face recognition methods usually assume that there are multiple samples per person (MSPP) available for discriminative feature extraction during the training phase. In many practical face recognition applications such as law enhancement, e-passport, and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning. To address this problem, we propose in this paper a novel discriminative multimanifold analysis (DMMA) method by learning discriminative features from image patches. First, we partition each enrolled face image into several nonoverlapping patches to form an image set for each sample per person. Then, we formulate the SSPP face recognition as a manifold-manifold matching problem and learn multiple DMMA feature spaces to maximize the manifold margins of different persons. Finally, we present a reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach.
机译:常规的基于外观的面部识别方法通常假定每人有多个样本(MSPP)可用于在训练阶段进行区分性特征提取。但是,在许多实际的人脸识别应用程序中,例如法律增强,电子护照和身份证识别,由于在这些系统中注册或记录的每人只有一个样本(SSPP),因此该假设可能不成立。在这种情况下,许多流行的面部识别方法无法正常工作,因为没有足够的样本进行判别式学习。为了解决这个问题,我们在本文中提出了一种新颖的判别多流形分析(DMMA)方法,该方法通过从图像块中学习判别特征来进行。首先,我们将每个已注册的人脸图像划分为几个不重叠的面片,以形成每个人每个样本的图像集。然后,我们将SSPP人脸识别公式化为流形歧管匹配问题,并学习多个DMMA特征空间以最大化不同人的流形边缘。最后,我们提出了一种基于重建的流形歧管距离来识别未标记的对象。提出了在三个广泛使用的人脸数据库上的实验结果,以证明该方法的有效性。

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