Identity-Free Facial Expression Recognition Using Conditional Generative Adversarial Network

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 des...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 1344 - 1348
Main Authors Cai, Jie, Meng, Zibo, Khan, Ahmed Shehab, O'Reilly, James, Li, Zhiyuan, Han, Shizhong, Tong, Yan
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.09.2021
Subjects
Conferences
Convolutional Neural Network
Face recognition
Facial Expression Generation
Facial Expression Recognition
Generative Adversarial Network
Generative adversarial networks
Image recognition
Lighting
Neural networks
Transforms
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Summary: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 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.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506593