Neighborhood repulsed correlation metric learning for kinship verification

In this paper, we propose a new neighborhood repulsed correlation metric learning (NRCML) method for kinship verification. While several metric learning algorithms have been proposed in recent years and some of them have successfully applied to kinship verification, most existing metric learning methods are developed based on the Euclidian similarity metric, which is not powerful enough to measure the similarity of face samples. To address this, we propose a NRCML method by using the correlation similarity measure to learn a discriminative distance metric, under which positive pairs are pulled as close as possible and negative pairs lying in a neighborhood are repulsed as far as possible, simultaneously. Experimental results are presented to show the effectiveness of the proposed method.