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Component-based metric learning for fully automatic kinship verification

机译：基于组件的公制学习，用于全自动血缘关系验证

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摘要

This paper introduces a fully automatic method for kinship verification from facial images. Recently, a number of methods have been proposed to verify kinship from facial images, however, most of these methods are needed to exactly align face images before feature extraction in a manual manner. Unlike these methods, our method does not depend on face alignment. Firstly, we localize several facial feature points by utilizing a facial feature detector to extract SIFT descriptor around each feature point of a face image. Lastly, two ways, feature combination and distance metric learning, are used to verify the kinship of a pair of face images. For feature combination, three simple strategies of feature combination and support vector machine classifier are used for kinship verification. For metric learning, we propose a component-based metric learning (CML) method to measure the distance of each face pair, which jointly learns multiple local distance metrics, and one specific distance metric for each facial feature point. Experimental results show the effectiveness of our proposed approach on two popular kinship datasets.
机译：本文介绍了面部图像的全自动验证方法。最近，已经提出了许多方法来验证来自面部图像的血缘关系，然而，需要大多数这些方法以手动方式在特征提取之前精确地对齐面部图像。与这些方法不同，我们的方法不依赖于面部对齐。首先，我们通过利用面部特征检测器来提取面部图像的每个特征点的SIFT描述符来定向几个面部特征点。最后，使用两种方式，特征组合和距离度量学习，用于验证一对面部图像的血缘关系。对于特征组合，特征组合和支持向量机分类器的三种简单策略用于亲属验证。对于公制学习，我们提出了一种基于组件的度量学习（CML）方法来测量每个面对对的距离，该距离与每个面部特征点的一个特定距离度量一起共同学习多个本地距离度量。实验结果表明我们所提出的方法在两个流行的亲属数据集上的有效性。

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