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

## TL;DR

This paper introduces a novel attribute-guided face synthesis method that improves facial expression recognition by bridging the gap between synthetic and real face images through domain adaptation, enhancing accuracy in various datasets.

## Contribution

It proposes a new face synthesis model for cross-domain translation and domain adaptation, improving expression recognition performance on real-world and in-the-wild datasets.

## Key findings

- Enhanced expression recognition accuracy on multiple datasets.
- Effective face synthesis that reduces domain discrepancy.
- Improved performance on in-the-wild driver emotion recognition.

## Abstract

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.