Component-based metric learning for fully automatic kinship verification

Abstract

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.