Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images
Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.
 N. Meeki, A. Amine, M. A. Boudia, and N. Meeki, “Deep Learning for Non Verbal Sentiment Analysis : Facial Emotional Expressions,” in GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saida., 2020, vol. 3014, pp. 1–11.
 S. Agarwal, B. Santra, and D. P. Mukherjee, “Anubhav: recognizing emotions through facial expression,” Vis. Comput., vol. 34, no. 2, pp. 177–191, 2018.
 M. M and M. A, “Facial geometric feature extraction based emotional expression classification using machine learning algorithms,” PLoS One, vol. 16, no. 2, pp. 1–12, 2021.
 A. Hassouneh, A. M. Mutawa, and M. Murugappan, “Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods,” Informatics Med. Unlocked, vol. 20, p. 100372, 2020.
 M. Bedeloglu et al., “Image-based Analysis of Emotional Facial Expressions in Full Face Transplants,” J. Med. Syst., vol. 42, no. 3, pp. 1–10, 2018.
 Y. Lu, S. Wang, W. Zhao, and Y. Zhao, “WGAN-Based Robust Occluded Facial Expression Recognition,” IEEE Access, vol. 7, pp. 93594–93610, 2019.
 M. Magdin, L. Benko, and Š. Koprda, “A case study of facial emotion classification using affdex,” Sensors, vol. 19, no. 9, pp. 1–17, 2019.
 D. M. Watson, B. B. Brown, and A. Johnston, “A data-driven characterisation of natural facial expressions when giving good and bad news,” PLoS Comput. Biol., vol. 16, no. 10, pp. 1–13, 2020.
 F. Qin, J. Guo, and W. Sun, “Object-oriented ensemble classification for polarimetric SAR Imagery using restricted Boltzmann machines,” Remote Sens. Lett., vol. 8, no. 3, pp. 204–213, 2017.
 L. Duran-Lopez, J. P. Dominguez-Morales, A. F. Conde-Martin, S. Vicente-Diaz, and A. Linares-Barranco, “PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection,” IEEE Access, vol. 8, pp. 128613–128628, 2020.
 G. H. de Rosa and J. P. Papa, “Soft-Tempering Deep Belief Networks Parameters Through Genetic Programming,” J. Artif. Intell. Syst., vol. 1, no. 1, pp. 43–59, 2019.
 D. Hamester, P. Barros, and S. Wermter, “Face expression recognition with a 2-channel Convolutional Neural Network,” Proc. Int. Jt. Conf. Neural Networks, vol. 2015-Septe, no. July, pp. 1787–1794, 2015.
 A. George and S. Marcel, “Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 361–375, 2021.
 B. K. Triwijoyo, “Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 18, no. 2, pp. 211–221, 2019.
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