Award Date

August 2023

Degree Type


Degree Name

Doctor of Philosophy (PhD)


Computer Science

First Committee Member

Fatma Nasoz

Second Committee Member

Kazem Taghva

Third Committee Member

Jan Pedersen

Fourth Committee Member

Mingon Kang

Fifth Committee Member

Emma Regentova

Number of Pages



Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. These results were compared to the state-of-the-art classification, which achieved a 75.2% accuracy. Our approach incorporates data augmentation techniques to mitigate overfitting and improve the performance of the CNN classifier. Additionally, we aim to bridge the gap between supervised and unsupervised learning in FER by integrating Generative Adversarial Networks (GANs) into our approach. GANs offer a unique approach to generate synthetic images and have been widely used in various computer vision applications. We provide a theoretical basis for GANs and demonstrate their application in generating realistic synthetic images using the FER2013 dataset. Furthermore, we replicate three popular GAN architectures using the MNIST dataset, validating the feasibility of using GANs for image generation and data augmentation in the context of FER tasks. Overall, our proposed approach combines CNNs for classification with GANs for data augmentation, aiming to improve the accuracy of FER models while mitigating overfitting. This has the potential to advance the understanding of human emotions through AI technologies and contribute to the field of FER. The utilization of GANs in FER research also opens possibilities for generating realistic images from other facial expression datasets, expanding the application of AI in this domain.


Data Augmentation; Emotion Detection with Artificial Intelligence-based Expression Analysis; Human-Computer Interaction; Image Classification; Machine / Deep Learning Techniques; Synthetic Image Generation


Artificial Intelligence and Robotics | Computer Engineering | Computer Sciences

File Format


File Size

36050 KB

Degree Grantor

University of Nevada, Las Vegas




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