Facial Expression Recognition of Industrial Internet of Things by Parallel Neural Networks Combining Texture Features

Zhenghao Xi · Yuhui Niu · Jieyu Chen · Xiu Kan · Huaping Liu

65 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

Industrial Internet of Things (IIoT) has been widely applied in smart home, smart city, smart traffic, etc. It is a big challenge to recognize facial expression of IIoT systems more effectively. Current facial recognition methods only utilize singular facial images, so accurate features that are highly correlated with facial changes can hardly be extracted. In order to overcome this difficulty and improve facial expression recognition, in this article, we propose a parallel neural network combining texture features, which can be applied in facial expression recognition. This parallel neural network is constructed by convolution neural network, residual network, and capsule network. Additionally, texture analysis is conducted on facial expression images to extract abundant features. Eight texture features are extracted by gray-level co-occurrence matrix and integrated with features of original images. Finally, these integrated features extracted by three kinds of networks are used to classify facial images. Experimental results prove that the proposed approach has a high recognition rate and strong robustness compared to competitive algorithms. Remarkably, our accuracy reaches 98.14%, with an increase of 3.71% in comparison with ResNet, and the F1-score of 0.9801. It is thus verified from this result that the proposed algorithm has many outstanding advantages. The idea in combination with texture features also provides a new solution for image classification.

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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 2015Image based Static Facial Expression Recognition with Multiple Deep Network Learning 2017FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition 2019LEARNet: Dynamic Imaging Network for Micro Expression Recognition

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