Abstract
Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative models, and each of them is designed to generate one of the prototypic facial expression images. Second, a regular CNN (FER-Net) is fine- tuned for expression classification. After the corresponding prototypic facial expressions are regenerated from each facial image, we output the last FC layer of FER-Net as features for both the input image and the generated images. Based on the minimum distance between the input image and the generated expression images in the feature space, the input image is classified as one of the prototypic expressions consequently. Our proposed method can not only alleviate the influence of inter-subject variations but will also be flexible enough to integrate with any other FER CNNs for person-independent facial expression recognition. Our method has been evaluated on CK+, Oulu-CASIA, BU-3DFE and BU-4DFE databases, and the results demonstrate the effectiveness of our proposed method.
Recommended Citation
H. Yang et al., "Identity-adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks," Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, pp. 294 - 301, Institute of Electrical and Electronics Engineers, Jun 2018.
The definitive version is available at https://doi.org/10.1109/FG.2018.00050
Department(s)
Computer Science
Keywords and Phrases
CNN; FER; GAN; Identity-adaptive
International Standard Book Number (ISBN)
978-153862335-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
05 Jun 2018
