Hierarchical Discriminant Feature Learning for Heterogeneous Face Recognition

Heterogeneous Face Recognition (HFR) refers to the problem of recognizing faces across different visual domains and has attached great attention owing to its tremendous potential benefits in practical applications. In this paper, a novel feature learning approach named hierarchical discriminant feature learning (HDFL) has been proposed for HFR. Different from traditional feature learning based HFR approaches, the proposed HDFL aims to learn the most discriminative information via a two-layer hierarchical boosting network (HBN), where the hierarchical discriminative information can be exploited in the learned features and the appearance difference can be effectively reduced, simultaneously. Extensive experiments on three different heterogeneous face databases demonstrate that our approach consistently outperforms the state-of-the-art methods.