Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network

TL;DR

This paper introduces IF-GAN, a novel identity-free facial expression recognition method using a conditional GAN to generate average identity faces, reducing inter-subject variation and improving FER accuracy.

Contribution

The paper presents a new identity-free FER approach with a cGAN that transforms faces to an average identity, enhancing recognition performance across diverse subjects.

Findings

Outperforms baseline CNN in FER tasks.

Achieves state-of-the-art results on four datasets.

Effective on datasets with spontaneous expressions.

Abstract

A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e.g., age, race, and gender. As part of an end-to-end system, a cGAN was designed to transform a given input facial expression image to an "average" identity face with the same expression as the input. Then, identity-free FER is possible since the generated images have the same synthetic "average" identity and differ only in their displayed expressions. Experiments on four facial expression datasets, one with spontaneous expressions, show that IF-GAN outperforms the baseline CNN and achieves state-of-the-art performance for FER.