# Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition

arXiv: https://arxiv.org/abs/2204.11434 · HTML: https://mlsanity.com/paper/2204.11434/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [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)
- [Dual Variational Generation for Low-Shot Heterogeneous Face Recognition](https://mlsanity.com/paper/1903.10203/) (2019)
- [Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks](https://mlsanity.com/paper/1812.05155/) (2018)
- [Cross-Domain Identification for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2008.08473/) (2020)
- [Disentangled Variational Representation for Heterogeneous Face Recognition](https://mlsanity.com/paper/1809.01936/) (2018)
- [Cross-Spectral Face Hallucination via Disentangling Independent Factors](https://mlsanity.com/paper/1909.04365/) (2019)
- [ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks](https://mlsanity.com/paper/1905.10620/) (2019)
- [Adversarial Discriminative Heterogeneous Face Recognition](https://mlsanity.com/paper/1709.03675/) (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)
- [Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition](https://mlsanity.com/paper/2005.11945/) (2020)
- [A NIR-to-VIS face recognition via part adaptive and relation attention module](https://mlsanity.com/paper/2102.00689/) (2021)

## Built on by (most cited first, 2 of 2)

- [SASMU: boost the performance of generalized recognition model using synthetic face dataset](https://mlsanity.com/paper/2306.01449/) (2023)
- [Adaptive Deep Iris Feature Extractor at Arbitrary Resolutions](https://mlsanity.com/paper/2407.08341/) (2024)

## Abstract (arXiv)

Face recognition for visible light (VIS) images achieve high accuracy thanks to the recent development of deep learning. However, heterogeneous face recognition (HFR), which is a face matching in different domains, is still a difficult task due to the domain discrepancy and lack of large HFR dataset. Several methods have attempted to reduce the domain discrepancy by means of fine-tuning, which causes significant degradation of the performance in the VIS domain because it loses the highly discriminative VIS representation. To overcome this problem, we propose joint feature distribution alignment learning (JFDAL) which is a joint learning approach utilizing knowledge distillation. It enables us to achieve high HFR performance with retaining the original performance for the VIS domain. Extensive experiments demonstrate that our proposed method delivers statistically significantly better performances compared with the conventional fine-tuning approach on a public HFR dataset Oulu-CASIA NIR&VIS and popular verification datasets in VIS domain such as FLW, CFP, AgeDB. Furthermore, comparative experiments with existing state-of-the-art HFR methods show that our method achieves a comparable HFR performance on the Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.