# Graphical Representation for Heterogeneous Face Recognition

arXiv: https://arxiv.org/abs/1503.00488 · HTML: https://mlsanity.com/paper/1503.00488/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2015.

## Built on by (most cited first, 5 of 5)

- [RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification](https://mlsanity.com/paper/2411.01225/) (2024)
- [Exploring Invariant Representation for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2302.00884/) (2023)
- [CLIP4Sketch: Enhancing Sketch to Mugshot Matching through Dataset Augmentation using Diffusion Models](https://mlsanity.com/paper/2408.01233/) (2024)
- [Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network](https://mlsanity.com/paper/2108.09898/) (2021)
- [Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator](https://mlsanity.com/paper/2111.14339/) (2021)

## Abstract (arXiv)

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.