Date of Award
12-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
Committee Chair/Advisor
Eric Patterson
Committee Member
Federico Iuricich
Committee Member
Joseph Kider
Committee Member
Matias Volonte
Abstract
Synthetic faces (e.g., computer-generated characters) have been increasingly utilized across various fields, including entertainment, healthcare, and education. Perceptual studies are often conducted to understand how synthetic faces are perceived by humans, aiming to enhance both quality and user experience in these domains. Over the years, numerous methods have been developed to create synthetic faces, ranging from traditional techniques such as image composites, Active Appearance Models, and 3D Morphable Models to more recent machine-learning-based frameworks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Despite the growing adoption of synthetic face generation and the variety of algorithms available for their creation, cognitive scientists have underutilized these advanced techniques. Research in this area has remained largely dependent on earlier approaches, and even when Artificial-Intelligence-based (e.g., AI-based) methods are employed, there is an overreliance on GANs, particularly StyleGAN2. This trend highlights a significant gap in the exploration of alternative generative architectures in perceptual studies. Furthermore, existing studies have primarily focused on the technical performance of GANs and VAEs, while human perception of their outputs has remained underexplored.
This dissertation seeks to address this gap by first providing a comprehensive review of synthetic face generation methods from a perceptual standpoint. Second, it analyzes and perceptually compares two prominent AI-based models: StyleGAN2 (a GAN variant) and NVAE (a VAE flavor) across multiple contexts (e.g., full scenes vs. isolated faces without background, and animated vs. static faces) to determine how these conditions influence perceived realism and trustworthiness. This comparison supports the development of cognitive research that advances the generation of perceptually engaging and practically useful synthetic faces.
Finally, conducting a study to investigate how truthful versus misleading medical information about dementia influenced participants’ perceptions when viewing videos of synthetic faces generated by StyleGAN2.
Finally, conducting a study to investigate how truthful versus misleading medical information about dementia influenced participants’ perceptions when viewing videos of synthetic faces generated by StyleGAN2.
The outcomes of this dissertation provide insights into the perceptual differences between GAN-based and VAE-based synthetic faces across diverse contexts. Understanding these distinctions will contribute to the responsible and effective application of synthetic faces in real-life applications.
Recommended Citation
Almaimani, Maha Habib, "An Analysis of Face Synthesis Methods and Their Influence on Human Perception" (2025). All Dissertations. 4131.
https://open.clemson.edu/all_dissertations/4131