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

Abstract views: 262 / PDF downloads: 274


  • Berkay Cakmak Erciyes University
  • Ibrahim Develi Erciyes University


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


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


W. Mellouk and W. Handouzi, Facial emotion recognition using deep learning: review and insights. Procedia Computer Science, 2020, 175: 689-694.

P. Ekman and W. V. Friesen, Constants across cultures in the face and emotion. Journal of personality and social psychology, 1971, 17.2: 124.

B. C. Ko, A brief review of facial emotion recognition based on visual information. sensors, 2018, 18.2: 401.

F. Altekin and H. Demir, Emotion Detection from Facial Expression Using Different Feature Descriptor Methods with Convolutional Neural Networks. European Journal of Engineering and Applied Sciences, 4.1: 14-17.

H. Yu, Network complexity analysis of multilayer feedforward artificial neural networks. Applications of Neural Networks in High Assurance Systems, 2010, 41-55.

Y. Zhao, et al., A faster algorithm for reducing the computational complexity of convolutional neural networks. Algorithms, 2018, 11.10: 159.

R. J. Cintra, et al. Low-complexity approximate convolutional neural networks. IEEE transactions on neural networks and learning systems, 2018, 29.12: 5981-5992.

N. Mehendale, Facial emotion recognition using convolutional neural networks (FERC). SN Applied Sciences, 2020, 2.3: 446.

J. H. Kim, Alwin Poulose and D. S. Han., The extensive usage of the facial image threshing machine for facial emotion recognition performance. Sensors, 2021, 21.6: 2026.

V. Tumen, O. F. Soylemez and B. Ergen, Facial emotion recognition on a dataset using convolutional neural network. In: 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. p. 1-5.

L. Zahara, et al. The facial emotion recognition (FER-2013) dataset for prediction system of micro-expressions face using the convolutional neural network (CNN) algorithm based Raspberry Pi. In: 2020 Fifth international conference on informatics and computing (ICIC). IEEE, 2020. p. 1-9.

I. Lasri, A. R. Solh and M. El Belkacemi, Facial emotion recognition of students using convolutional neural network. In: 2019 third international conference on intelligent computing in data sciences (ICDS). IEEE, 2019. p. 1-6.

M. I. Georgescu., R. T. Ionescu and M. Popescu, Local learning with deep and handcrafted features for facial expression recognition. IEEE Access, 2019, 7: 64827-64836.

T. Connie, et al. Facial expression recognition using a hybrid CNN–SIFT aggregator. In: Multi-disciplinary Trends in Artificial Intelligence: 11th International Workshop, MIWAI 2017, Gadong, Brunei, November 20-22, 2017, Proceedings 11. Springer International Publishing, 2017. p. 139-149.

W. Wang, et al. Emotion recognition of students based on facial expressions in online education based on the perspective of computer simulation. Complexity, 2020, 2020: 1-9.




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