AbdElminaam, D., Mostafa, S., Tamer Ghareeb, B., Tarek, F., Said, H. (2023). FER_ML: Facial Emotion Recognition using Machine Learning. test, 2(1), 40-49. doi: 10.21608/jocc.2023.282094
Diaa s AbdElminaam; shihab Mostafa; Bilal Tamer Ghareeb; FAdy Tarek; HAshim Said. "FER_ML: Facial Emotion Recognition using Machine Learning". test, 2, 1, 2023, 40-49. doi: 10.21608/jocc.2023.282094
AbdElminaam, D., Mostafa, S., Tamer Ghareeb, B., Tarek, F., Said, H. (2023). 'FER_ML: Facial Emotion Recognition using Machine Learning', test, 2(1), pp. 40-49. doi: 10.21608/jocc.2023.282094
AbdElminaam, D., Mostafa, S., Tamer Ghareeb, B., Tarek, F., Said, H. FER_ML: Facial Emotion Recognition using Machine Learning. test, 2023; 2(1): 40-49. doi: 10.21608/jocc.2023.282094

FER_ML: Facial Emotion Recognition using Machine Learning

Journal of Computing and Communication
Article 5, Volume 2, Issue 1, January 2023, Pages 40-49    PDF (481.83 K)
Document Type: Original Article
DOI: 10.21608/jocc.2023.282094
Authors
Diaa s AbdElminaam* 1; shihab Mostafa2; Bilal Tamer Ghareeb3; FAdy Tarek2; HAshim Said2
1Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt
2Faculty of Computer Science Misr International University, Cairo, Egypt
3Faculty of Computer Science Misr International University
Abstract
Recently, facial recognition has been one of the most crucial technologies people need. Facial recognition has attracted a lot of the crowd; for example, it has been used in security on most modern devices. Using machine and deep learning, overall performance will be improved, and the identification accuracy will be more precise. We aim to discover how well these algorithms perform in classifying human facial expressions and whether or not we can depend on them. The steps are as follows. First, we embed the images from the dataset, then split the dataset into 70% training data and 30% testing data; after that, we apply five different algorithms: Support Vector Machine, K-nearest Neighbor, Logistic Regression, Naive Bayes, and Random Forest. Support Vector Machine achieved an accuracy rate of 36%, K-nearest Neighbor achieved an accuracy rate of 52.3%, Logistic regression achieved an accuracy rate of 64.2%, and Naive Bayes achieved an accuracy rate of 38.1%. Random Forest achieved an accuracy rate of 51.7%. The dataset used was a cleaned version of the FER13 dataset, which contains 16,780 images divided into five classes (angry, happy, neutral, disgust, and fear). The results show that Logistic Regression proved to be the most accurate classifier among the presented ones, with an F1-Score of 63.8% and an accuracy of 64.2%.
Keywords
Support Vector Machine (SVM); Naive Bayes(NB) K-nearest Neighbor (KNN); Logistic Regression (LR) Random Forest (RF) Machine Learning Facial Emotion Recognition
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