Convolutional Neural Network-Based Classification of Facial Emotional Expressions and Computational Complexity Analysis


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Authors

  • Berkay Cakmak Erciyes University
  • Ibrahim Develi Erciyes University

Keywords:

Convolutional Neural Networks, Identifying Facial Expressions of Emotions, FER2013 Data Set, Complexity Analysis, Online Education Efficiency

Abstract

This study presents a novel convolutional neural network (CNN) model and a detailed complexity analysis with other models available in the literature for an accurate classification of facial emotional expressions. Human beings have been defined by seven basic emotions, which are anger, fear, happiness, sad, contempt, disgust, and surprise. Model accuracy plays an important role in emotion detection studies with deep neural networks, as the high model accuracy is directly related to the accuracy of the predicted emotions. A 23-layer CNN model was created that classifies 7 different emotions. The CNN model we trained with the FER2013 dataset has a higher accuracy performance than other studies trained with the same dataset in the literature. The accuracy performance of our CNN model is 98.83% in training data and 83.52% in validation data. The complexity of the algorithms used in other studies is compared with the proposed study. Although the accuracy performance of our CNN model is higher than other studies in the literature, the complexity of our model is also higher than most other studies. The CNN model we obtained is used in an algorithm that we have created to increase the efficiency of online courses, which performs sentiment analysis 4 times per second.

Author Biographies

Berkay Cakmak, Erciyes University

Faculty of Engineering, Electrical & Electronics Engineering, 38039, Kayseri, Türkiye

Ibrahim Develi, Erciyes University

Faculty of Engineering, Electrical & Electronics Engineering, 38039, Kayseri, Türkiye

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Published

2023-02-13

How to Cite

Cakmak, B., & Develi, I. (2023). Convolutional Neural Network-Based Classification of Facial Emotional Expressions and Computational Complexity Analysis. International Conference on Frontiers in Academic Research, 1, 168–173. Retrieved from https://as-proceeding.com/index.php/icfar/article/view/63