# Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator

arXiv: https://arxiv.org/abs/2111.14339 · HTML: https://mlsanity.com/paper/2111.14339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition](https://mlsanity.com/paper/1708.02412/) (2017)
- [DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition](https://mlsanity.com/paper/2009.09399/) (2020)
- [Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture](https://mlsanity.com/paper/1706.06247/) (2017)
- [Shared Representation Learning for Heterogeneous Face Recognition](https://mlsanity.com/paper/1406.1247/) (2014)
- [Relational Deep Feature Learning for Heterogeneous Face Recognition](https://mlsanity.com/paper/2003.00697/) (2020)
- [Sejong Face Database: A Multi-Modal Disguise Face Database](https://mlsanity.com/paper/2106.07186/) (2021)
- [Infrared face recognition: a comprehensive review of methodologies and databases](https://mlsanity.com/paper/1401.8261/) (2014)
- [Graphical Representation for Heterogeneous Face Recognition](https://mlsanity.com/paper/1503.00488/) (2015)
- [Deep Perceptual Mapping for Cross-Modal Face Recognition](https://mlsanity.com/paper/1601.05347/) (2016)

## Abstract (arXiv)

Visible-to-thermal face image matching is a challenging variate of cross-modality recognition. The challenge lies in the large modality gap and low correlation between visible and thermal modalities. Existing approaches employ image preprocessing, feature extraction, or common subspace projection, which are independent problems in themselves. In this paper, we propose an end-to-end framework for cross-modal face recognition. The proposed algorithm aims to learn identity-discriminative features from unprocessed facial images and identify cross-modal image pairs. A novel Unit-Class Loss is proposed for preserving identity information while discarding modality information. In addition, a Cross-Modality Discriminator block is proposed for integrating image-pair classification capability into the network. The proposed network can be used to extract modality-independent vector representations or a matching-pair classification for test images. Our cross-modality face recognition experiments on five independent databases demonstrate that the proposed method achieves marked improvement over existing state-of-the-art methods.