Multi-View Geometric Mean Metric Learning for Kinship Verification

Abstract

This paper proposes a multi-view geometric mean metric learning (MvGMML) method for the real-world kinship verification from facial images. Unlike existing kinship verification methods which dramatically degrade their performance when facial images are not well aligned, we present an efficient misalignment-robust kinship verification framework. First, a facial feature detector is employed to localize several facial feature points such as the right and left corners of two eyes. Then, a dense SIFT descriptor is extracted around each feature point. Lastly, our proposed MvGMML method jointly learns multiple local geometric mean metrics, one geometric mean metric for each view (i.e., feature point), to better exploit complementary information of all views. Experimental results on two widely used kinship datasets are presented to show the efficacy of our method.