Document Type : Original Research Paper


1 Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

2 Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

3 School of Engineering, London South Bank University, London, UK.


Background and Objectives: The recognition of facial expressions using metaheuristic algorithms is a research topic in the field of computer vision. This article presents an approach to identify facial expressions using an optimized filter developed by metaheuristic algorithms.
Methods: The entire process of feature extraction hinges on using a filter optimally configured by metaheuristic algorithms. Essentially, the purpose of utilizing this metaheuristic algorithm is to determine the optimal weights for feature extraction filters. Once the optimal weights for the filter have been determined by the metaheuristic algorithm, optimal filter sizes have also been determined. As an initial step, the k-nearest neighbor classifier is employed due to its simplicity and high accuracy. Following the initial stage, a final model is presented, which integrates results from both filterbank and Multilayer Perceptron neural networks.
Results: An analysis of the existing instances in the FER2013 database has been conducted using the method proposed in this article. This model achieved a recognition rate of 78%, which is superior to other algorithms and methods while requiring less training time than other algorithms and methods.In addition, the JAFFE database, a Japanese women's database, was utilized for validation. On this dataset, the proposed approach achieved a 94.88% accuracy rate, outperforming other competitors.
Conclusion: The purpose of this article is to propose a method for improving facial expression recognition by using an optimized filter, which is implemented through a metaheuristic algorithm based on the KA. In this approach, optimized filters were extracted using the metaheuristic algorithms kidney, k-nearest neighbor, and multilayer perceptron. Additionally, by employing this approach, the optimal size and number of filters for facial state recognition were determined in order to achieve the highest level of accuracy in the extraction process.


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[2] S. Kaur, N. Kulkarni, “Emotion recognition-a review,” Int. J. Appl. Eng. Res., 16(2): 103-110, 2021.
[4] P. Singh, R. K. Mishra, S. Urolagin, V. Sharma, “Enhancing security by identifying facial check-in using deep convolutional neural network,” in Proc. 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N): 1006-1010, 2021.


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