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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neural Technology
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1374112

Facial Emotion Recognition based on SOAR Model

Provisionally accepted
Matin Ramzani Shahrestani Matin Ramzani Shahrestani 1Sara Motamed Sara Motamed 2*Mohammadreza Yamaghani Mohammadreza Yamaghani 3*
  • 1 Department of Computer Engineering, Islamic Azad University, Rasht Branch, Rasht, Iran
  • 2 Department of Computer Engineering, Islamic Azad University, Fuman and Shaft branch, Fouman, Iran
  • 3 Faculty of Technical Engineering, Islamic Azad University of Lahijan, Lahijan, Gilan, Iran

The final, formatted version of the article will be published soon.

    Expressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human-machine interaction is the recognition of facial expressions and the creation of feedback according to the perceived emotion. Due to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features and finally increasing the recognition rate. In order to implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images and considering the temporal information in dynamic images leads to more efficient and better classification. Also, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased and the working memory part of the SOAR model can be implemented. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. In order to compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models.

    Keywords: Facial emotional recognition, 3D Convolutional Neural Network (3DCNN), Learning Automata (LA), SOAR model, deep learning

    Received: 21 Jan 2024; Accepted: 01 May 2024.

    Copyright: © 2024 Ramzani Shahrestani, Motamed and Yamaghani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Sara Motamed, Department of Computer Engineering, Islamic Azad University, Fuman and Shaft branch, Fouman, Iran
    Mohammadreza Yamaghani, Faculty of Technical Engineering, Islamic Azad University of Lahijan, Lahijan, Gilan, Iran

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.