Local Large-Margin Multi-Metric Learning for Face and Kinship Verification

Metric learning has attracted wide attention in face and kinship verification and a number of such algorithms have been presented over the past few years. However, most existing metric learning methods learn only one Mahalanobis distance metric from a single feature representation for each face image and cannot make use of multiple feature representations directly. In many face-related tasks, we can easily extract multiple features for a face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features so that more discriminative information can be exploited than those learned from individual features. To achieve this, we present a large-margin multi-metric learning (LML) method for face and kinship verification, which jointly learns multiple global distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive pair is less than a low threshold and that of each negative pair is greater than a high threshold. To better exploit the local structures of face images, we also propose a local metric learning (LML) and a local large-margin multi-metric learning (LML) methods to learn a set of local metrics. Experimental results on three face datasets show that the proposed methods achieve very competitive results compared with the state-of-theart methods.