Identity-Adaptive Facial Expression Recognition through Expression Regeneration Using Conditional Generative Adversarial Networks

Huiyuan Yang · Lijun Yin

111 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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

Abstract

Subject variation is a challenging issue for fa- cial expression recognition, especially when handling unseen subjects with small-scale lableled 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.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2016Going deeper in facial expression recognition using deep neural networks 2016Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order 2015Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition 2017FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition 2015Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis 2017Identity-Aware Convolutional Neural Network for Facial Expression Recognition 2016Peak-Piloted Deep Network for Facial Expression Recognition

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2021Facial Expression Recognition: A Review of Trends and Techniques 2024GPT-4V with emotion: A zero-shot benchmark for Generalized Emotion Recognition 2021Identity-Free Facial Expression Recognition Using Conditional Generative Adversarial Network

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