Learning discriminative compact binary face descriptor for kinship verification

Abstract In this paper, we propose a weakly-supervised feature learning method called discriminative compact binary face descriptor (D-CBFD) for facial kinship verification. Unlike most existing kinship verification methods where hand-crafted features are used for face representation, our D-CBFD learns discriminative face representation from a set of weakly-labeled samples. Given a face image, we first compute pixel difference vectors (PDVs) at local patches. Then, we learn a discriminative projection to map each PDV into a low-dimensional binary feature space, where the energy information of the PDV can be well preserved and the distance between the positive pair is smaller and that between the negative pair is larger. Lastly, we pool these binary feature vectors within each face into a histogram feature as the final representation. Experimental results on three kinship datasets are presented to show the effectiveness of the proposed method.