Date of Award
5-2023
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Electrical Engineering
Department
Electrical Engineering
Major Professor
Dr. Dimitrios Charalampidis
Second Advisor
Dr. Abdul Rahman Alsamman
Third Advisor
Dr. Kim Jovanovich
Abstract
Facial emotion recognition is a widely studied area with applications in diverse domains such as human-computer interaction, affective computing, and social robotics. This thesis aims to improve the accuracy of facial emotion recognition models by incorporating a second neural network trained on original probabilities and probability transformation, while also comparing the performance of different techniques. The thesis begins with a thorough review of available datasets and technologies used for data collection, highlighting the challenges associated with these datasets. A detailed analysis of various facial emotion detection models, including the baseline model and its different architectures, is presented. The thesis also explores the pre-processing of datasets for binary classifiers and investigates the effects of developing an ensemble of binary classifiers.The main contribution of the thesis is the incorporation of a second neural network trained on the probabilities of binary models, along with probability transformation, to enhance the accuracy of facial emotion recognition models. Experimental results on the FER2013 dataset are presented, demonstrating the effectiveness of this approach, achieving a best accuracy of 69.4%. Additionally, the thesis compares the performance of different techniques to provide insights into their relative effectiveness in improving facial emotion recognition accuracy.The thesis concludes with a summary of the results, drawing conclusions from the analysis, and discussing future directions for further research in facial emotion recognition. The findings of this research contribute to the advancement of facial emotion recognition techniques and provide valuable insights for researchers and practitioners in the field.
Recommended Citation
Hanin, Arsany, "Comparison of Facial Emotion Recognition Models Using Deep Learning" (2023). University of New Orleans Theses and Dissertations. 3076.
https://scholarworks.uno.edu/td/3076
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.