Master’s Thesis: Comprehensive Facial Data and Gaze Extraction Using iPhone Front Camera and ARKit

23.03.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

This research aims to explore the full potential of the iPhone front camera and ARKit for extracting various facial attributes, including:

Face orientation (head pose tracking)
Gaze estimation (eye-tracking and attention focus)

By leveraging ARKit's advanced face-tracking capabilities, our goal is to collect, analyze, and evaluate facial data under various conditions, exploring its potential applications in human-computer interaction (HCI) and accessibility solutions.

Your goal is to extract the facial features and robustly estimate the gaze direction and head orientation in a real-world scenario. The solutions should be evaluated using ground truth data from external eye-tracking devices or computer vision models.

Prerequisites:

Experience with OpenCV, Python, and Swift.
Knowledge of ARKit would be beneficial.
Self-motivated and independent approach to work.

Kontakt: tim.schreiter@tum.de

Mehr Information

https://www.ce.cit.tum.de/pins/open-positions/student-positions/