Binary cross coupled discriminant analysis for visual kinship verification

Abstract

Kinship verification refers to comparing similarities between two different individuals through their facial images. In this context, feature descriptors play a crucial role, and few feature descriptors are present in literature to extract kin features from facial images. In this paper, we propose a binary cross-coupled discriminant analysis (BC2DA) based feature descriptor which is able to extract effective kin features from input facial image pairs. This method reduces the discrimination between kin pairs at the feature extraction stage itself. BC2DA converts original kin image pairs to encoded image pairs to reduce the discrimination between them. To make better use of tri-subject kin relations, we further propose multi cross-coupled discriminant analysis (MC2DA). This method reduces the discrimination between child and both parents’ images at the feature extraction stage. Extensive experiments were conducted on six kinship datasets such as KinfaceW-I/II, Cornell, FIW, TSKinface UBKinface to show the efficacy of the proposed algorithm.