Kinship verification using neighborhood repulsed correlation metric learning

Kinship verification is an interesting and challenging problem in human face analysis, which has received increasing interests in computer vision and biometrics in recent years. This paper presents a neighborhood repulsed correlation metric learning (NRCML) method for kinship verification via facial image analysis. Most existing metric learning based kinship verification methods are developed with the Euclidian similarity metric, which is not powerful enough to measure the similarity of face samples, especially when they are captured in wild conditions. Motivated by the fact that the correlation similarity metric can better handle face variations than the Euclidian similarity metric, we propose a NRCML method by using the correlation similarity measure where the kin relation of facial images can be better highlighted. Since negative kinship samples are usually less than positive samples, we automatically identify the most discriminative negative samples in the training set to learn the distance metric so that the most discriminative encoded by negative samples can better exploited. Experimental results show the efficacy of the proposed approach.