Thesis Description & Objectives
Context: The increasing integration of advanced technologies in various industries necessitates robust validation methods
for computer vision systems. A recent initiative aims to leverage generative AI to create a controllable simulator that
can enhance the validation of face recognition systems. This approach addresses challenges such as
environmental lighting variability and changes in user appearance, which are critical for ensuring the accuracy and
reliability of biometric systems.
Project description: This project will explore the use of generative AI as a controllable simulator for validating computer vision systems,
with a particular focus on face recognition technologies. The research aims to evaluate the effectiveness of
generative models by examining various aspects such as semantic fidelity, which includes identity and scene
coherence; geometric fidelity, focusing on pose and multi-view consistency; and photometric fidelity, which
analyzes lighting and lens effects. Additionally, the project will assess physical fidelity through the control of camera
parameters, ground-truth reliability by measuring the accuracy of measurements, and dataset quality by
emphasizing diversity and scalability.
Objectives:
• To evaluate the effectiveness of genAI in creating controllable synthetic data for computer vision validation.
• To establish a comprehensive evaluation framework based on the proposed taxonomy.
• To generate a dataset that meets requirements for effective validation of face recognition systems.
• To explore potential applications of the findings in other industries, including automotive and beyond.
