Efficient Facial Expression Recognition With Representation Reinforcement Network and Transfer Self-Training for Human–Machine Interaction

Cheng-Shan Jiang · Zhentao Liu · Min Wu · Jinhua She · Weihua Cao

41 citations0/0 files storedIEEE Transactions on Industrial Informatics

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

Abstract

The emotion perception capability could endow machines with deeper feedback evaluation of human–machine interaction (HMI), which makes the experience of HMI more harmonious and natural. Currently, how to obtain the robust surface and semantic facial representation features has not been fully explored, moreover, the parameters and computation of conventional convolutional neural network models, which are adopted in facial expression recognition (FER) are too heavy, it is not conducive to the deployment of the HMI system. Therefore, this article proposes a representation reinforcement network (RRN) and transfer self-training (TST) based efficient FER method. Our designed RRN is mainly composed of two modules, i.e., surface representation reinforcement (SurRR) module and semantic representation reinforcement (SemaRR) module. SurRR module fuses the facial features in different dimensions and highlights key feature messaging nodes in feature maps. SemaRR module establishes the deep semantic representation of global facial regions on spatial and channel dimensions. Therefore, RRN has stronger feature extraction capability when the parameters and computational complexity are significantly reduced, and TST is proposed for contributing to the training effect of RRN at the condition of insufficient training data. We tested our method on the extended Cohn-Kanade dataset (CK+), Radboud faces database (RaFD), FERPLUS, and the real-world affective faces database (RAF-DB) datasets, and the recognition accuracy are 100%, 98.62%, 89.64%, and 88.72%, respectively. Furthermore, the preliminary application experiment, which demonstrates the feasibility of our method in practice during HMI.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild 2020Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition 2016Training deep networks for facial expression recognition with crowd-sourced label distribution 2018Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognition 2018Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition 2021Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild

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