# Heterogeneous Face Frontalization via Domain Agnostic Learning

arXiv: https://arxiv.org/abs/2107.08311 · HTML: https://mlsanity.com/paper/2107.08311/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis](https://mlsanity.com/paper/1704.04086/) (2017)
- [Towards Large-Pose Face Frontalization in the Wild](https://mlsanity.com/paper/1704.06244/) (2017)
- [Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks](https://mlsanity.com/paper/1812.05155/) (2018)
- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis](https://mlsanity.com/paper/1904.07344/) (2019)
- [Dual-Attention GAN for Large-Pose Face Frontalization](https://mlsanity.com/paper/2002.07227/) (2020)
- [TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition](https://mlsanity.com/paper/1712.02514/) (2017)
- [Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision](https://mlsanity.com/paper/2008.06843/) (2020)
- [Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces](https://mlsanity.com/paper/1708.02681/) (2017)
- [Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization](https://mlsanity.com/paper/1806.08472/) (2018)
- [Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis](https://mlsanity.com/paper/2004.09502/) (2020)
- [Thermal to Visible Synthesis of Face Images using Multiple Regions](https://mlsanity.com/paper/1803.07599/) (2018)
- [Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis](https://mlsanity.com/paper/1901.00889/) (2019)
- [LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition](https://mlsanity.com/paper/1912.07809/) (2019)
- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis](https://mlsanity.com/paper/1904.00168/) (2019)
- [Facial Synthesis from Visual Attributes via Sketch using Multi-Scale Generators](https://mlsanity.com/paper/1912.10479/) (2019)
- [Multimodal Face Synthesis from Visual Attributes](https://mlsanity.com/paper/2104.04362/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Synthesis-Based Approach for Thermal-to-Visible Face Verification](https://mlsanity.com/paper/2108.09558/) (2021)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation](https://mlsanity.com/paper/2311.01929/) (2023)
- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)

## Abstract (arXiv)

Recent advances in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on thermal to visible face synthesis and matching problems. However, current DCNN-based synthesis models do not perform well on thermal faces with large pose variations. In order to deal with this problem, heterogeneous face frontalization methods are needed in which a model takes a thermal profile face image and generates a frontal visible face. This is an extremely difficult problem due to the large domain as well as large pose discrepancies between the two modalities. Despite its applications in biometrics and surveillance, this problem is relatively unexplored in the literature. We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis. A contrastive constraint is enforced in the latent space of the generator with the help of a dual-path training strategy, which improves the feature vector discrimination. Finally, a multi-purpose loss function is utilized to guide the network in synthesizing identity preserving cross-domain frontalization. Extensive experimental results demonstrate that DAL-GAN can generate better quality frontal views compared to the other baseline methods.