SpecTracle: Wearable Facial Motion Tracking from Unobtrusive Peripheral Cameras

TL;DR

SpecTracle introduces a real-time, unobtrusive facial motion tracking system using peripheral cameras on AR glasses, enabling full-face tracking without bulky equipment, with improved accuracy through personalized calibration.

Contribution

The paper presents a novel, low-profile facial tracking system using peripheral cameras and neural networks, suitable for AR glasses, with real-time performance and user-specific calibration.

Findings

Achieves 24 fps real-time tracking on mobile GPU.

Improves tracking accuracy by 42.3% with personalized calibration.

Enables full-face motion tracking without intrusive cameras.

Abstract

Facial motion tracking in head-mounted displays (HMD) has the potential to enable immersive "face-to-face" interaction in a virtual environment. However, current works on facial tracking are not suitable for unobtrusive augmented reality (AR) glasses or do not have the ability to track arbitrary facial movements. In this work, we demonstrate a novel system called SpecTracle that tracks a user's facial motions using two wide-angle cameras mounted right next to the visor of a Hololens. Avoiding the usage of cameras extended in front of the face, our system greatly improves the feasibility to integrate full-face tracking into a low-profile form factor. We also demonstrate that a neural network-based model processing the wide-angle cameras can run in real-time at 24 frames per second (fps) on a mobile GPU and track independent facial movement for different parts of the face with a user-independent model. Using a short personalized calibration, the system improves its tracking performance by 42.3% compared to the user-independent model.