Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets
Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets
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DOI：
10.1109/tip.2017.2746993
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发表时间：
2015-06
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影响因子：
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通讯作者：
Ruiping Wang;Zhiwu Huang;S. Shan;Xilin Chen
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文献类型：
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作者：
Ruiping Wang;Zhiwu Huang;S. Shan;Xilin Chen
本文提出了一种基于高斯分布黎曼流形的判别分析方法（DARG）来解决图像集的人脸识别问题。我们的目标是捕获每个集合中的底层数据分布，从而促进更鲁棒的分类。为此，我们将图像集表示为高斯混合模型（GMM），该模型包括一些具有先验概率的高斯分量，并试图区分不同类别的高斯分量。根据信息几何，高斯分布在一个特定的黎曼流形上。为了正确地编码这样的黎曼几何，我们研究了高斯之间的几个距离，并进一步推导出一系列可证明的正定概率核。通过这些核，加权核判别分析，最终设计了一个处理的高斯在高斯的样本和他们的先验概率作为样本权重。在四个最具挑战性和最大的数据库，YouTube名人，考克斯，YouTube脸DB和点和射击挑战，所提出的方法进行评估的人脸识别和验证任务，以证明其优越性的国家的最先进的。
This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. Through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.