Coupled Discriminant Analysis for Heterogeneous Face Recognition

Abstract

Coupled space learning is an effective framework for heterogeneous face recognition. In this paper, we propose a novel coupled discriminant analysis method to improve the heterogeneous face recognition performance. There are two main advantages of the proposed method. First, all samples from different modalities are used to represent the coupled projections, so that sufficient discriminative information could be extracted. Second, the locality information in kernel space is incorporated into the coupled discriminant analysis as a constraint to improve the generalization ability. In particular, two implementations of locality constraint in kernel space (LCKS)-based coupled discriminant analysis methods, namely LCKS-coupled discriminant analysis (LCKS-CDA) and LCKS-coupled spectral regression (LCKS-CSR), are presented. Extensive experiments on three cases of heterogeneous face matching (high versus low image resolution, digital photo versus video image, and visible light versus near infrared) validate the efficacy of the proposed method.