Graphical Representation for Heterogeneous Face Recognition

TL;DR

This paper introduces a novel graphical representation method for heterogeneous face recognition that leverages Markov networks to incorporate spatial information, significantly improving recognition accuracy across various challenging scenarios.

Contribution

The paper proposes a new graphical representation approach using Markov networks and a coupled similarity metric for heterogeneous face recognition, addressing limitations of previous methods.

Findings

Outperforms state-of-the-art HFR methods in multiple scenarios

Effectively incorporates spatial compatibility between image patches

Demonstrates robustness across diverse heterogeneous image types

Abstract

Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.