Dictionary Alignment for Low-Resolution and Heterogeneous Face Recognition

Mudunuri, Sivaram Prasad and Biswas, Soma (2017) Dictionary Alignment for Low-Resolution and Heterogeneous Face Recognition. In: 17th IEEE Winter Conference on Applications of Computer Vision (WACV), MAR 24-31, 2017, Santa Rosa, CA, pp. 1115-1123.

PDF 2017_IEEE_Win_Con_App_Com_Vis_1115.pdf - Published Version Restricted to Registered users only Download (617kB) | Request a copy

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.

Item Type:	Conference Proceedings
Series.:	IEEE Winter Conference on Applications of Computer Vision
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	22 Jul 2017 07:06
Last Modified:	22 Jul 2017 07:06
URI:	http://eprints.iisc.ac.in/id/eprint/57479

Actions (login required)

View Item