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

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.

Automated summary

This study proposes a parallel neural network combining texture features to improve facial expression recognition, achieving a high accuracy of 98.14% compared to ResNet. The results demonstrate the effectiveness and robustness of the proposed method in facial expression classification.