Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network

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.

References in corpus (2)

• Conditional Generative Adversarial Nets
• Deep Facial Expression Recognition: A Survey

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• Facial Expression Representation Learning by Synthesizing Expression Images
• Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal