Graphical Representation for Heterogeneous Face Recognition
This addresses the challenge of matching face images from different sources in biometrics, with incremental improvements in representation and similarity metrics.
The paper tackles the problem of heterogeneous face recognition (HFR) by proposing a graphical representation method (G-HFR) that uses Markov networks to represent image patches with spatial compatibility, resulting in outperforming state-of-the-art methods across multiple HFR scenarios.
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.