Dictionary Alignment for Low-Resolution and Heterogeneous Face Recognition

Abstract

Cross-domain matching is a challenging problem with several applications like face recognition across pose and resolution, heterogeneous face recognition, etc. Coupled dictionary learning has emerged as a powerful technique for addressing such problems. A novel approach based on aligning two orthogonal dictionaries constructed independently from the two domains is proposed in this work. Once the dictionaries are constructed, the correspondence between the dictionary atoms of the two domains are computed using bipartite graph matching in a common space. A Mahalanobis metric is then derived from sparse coefficient vectors of the aligned dictionaries of the two domains such that the coefficients from data of same class move closer and that of different classes move apart. Unlike other coupled dictionary learning approaches, one-to-one paired training data is not required in the proposed approach. Extensive experiments on MultiPIE, SCFace and MBGC database for face recognition across pose and resolution, CASIA NIRVIS 2.0 database for matching visible to near-infrared face images show the usefulness of the proposed approach for different applications.