Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network

Jie Cai · Zibo Meng · Ahmed Shehab Khan · Zhiyuan Li · James D O’Reilly · Shizhong Han +1 more

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

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.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2002Comprehensive database for facial expression analysis 2020Deep Facial Expression Recognition: A Survey 2017Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild 2005Web-Based Database for Facial Expression Analysis 2020Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition 2018Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism 2016Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order

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2021Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition

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