# Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

arXiv: https://arxiv.org/abs/1905.08090 · HTML: https://mlsanity.com/paper/1905.08090/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 7 of 7)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation](https://mlsanity.com/paper/2103.03781/) (2021)
- [Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data](https://mlsanity.com/paper/2412.01383/) (2024)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays](https://mlsanity.com/paper/2102.09895/) (2021)
- [Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images](https://mlsanity.com/paper/2104.02663/) (2021)
- [On Applicability of Synthetic Datasets for Facial Expression Recognition](https://mlsanity.com/paper/2605.17483/) (2026)

## Abstract (arXiv)

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. However, learning from synthetic face images can be problematic due to the distribution discrepancy between low-quality synthetic images and real face images and may not achieve the desired performance when the learned model applies to real world scenarios. To this end, we propose a new attribute guided face image synthesis to perform a translation between multiple image domains using a single model. In addition, we adopt the proposed model to learn from synthetic faces by matching the feature distributions between different domains while preserving each domain's characteristics. We evaluate the effectiveness of the proposed approach on several face datasets on generating realistic face images. We demonstrate that the expression recognition performance can be enhanced by benefiting from our face synthesis model. Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.