Volume 15, Issue 4 (10-2023)                   2023, 15(4): 25-31 | Back to browse issues page

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Khani Yengikand A, Farrokhi Afsharyan M, Nejati P. Facial Expression Recognition Based on Separable Convolution Network and Attention Mechanism. International Journal of Information and Communication Technology Research 2023; 15 (4) : 31
URL: http://ijict.itrc.ac.ir/article-1-566-en.html
Facial Expression Recognition Based on Separable Convolution Network and Attention Mechanism
Amir Khani Yengikand 1, Mostafa Farrokhi Afsharyan2 , Payam Nejati3
1- Department of Computer Engineering, University of Zanjan, Zanjan, Iran , amirkhani@znu.ac.ir
2- Department of Electrical Engineering, Shahid Sattari Aeronautical University Tehran, Iran
3- Department of Electrical Engineering, Malek Ashtar University of Technology, Tehran, Iran
Abstract:   (841 Views)
Facial expression recognition using deep learning methods has been one of the active research fields in the last decade. However, most of the previous works have focused on the implementation of the model in the laboratory environment, and few researchers have addressed the real-world challenges of facial expression recognition systems. One of the challenges of implementing the face recognition system in the real environment (e.g. webcam or robot) is to create a balance between accuracy and speed of model recognition. Because, increasing the complexity of the neural network model leads to an increase in the accuracy of the model, but due to the increase in the size of the model, the recognition speed of the model decreases. Therefore, in this paper, we propose a model to recognize the seven main emotions (Happiness, sadness, anger, surprise, fear, disgust and natural), which can create a balance between accuracy and recognition speed. Specifically, the proposed model has three main components. First, in the feature extraction component, the features of the input images are extracted using a combination of normal and separable convolutional networks. Second, in the feature integration component, the extracted features are integrated using the attention mechanism. Finally, the merged features are used as the input of the multi-layer perceptron neural network to recognize the input facial expression. Our proposed approach has been evaluated using three public datasets and images received via webcam
Article number: 31
Keywords: Facial expression recognition, Attention mechanism, Separable convolutional
Full-Text [PDF 765 kb]   (329 Downloads)    
Type of Study: Research | Subject: Information Technology
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