Cascaded Continuous Regression for Real-time Incremental Face Tracking

TL;DR

This paper presents a real-time facial landmark tracking algorithm that uses cascaded continuous regression with incremental learning, significantly improving speed and accuracy for online face tracking.

Contribution

It introduces cascaded continuous regression (CCR) and its incremental version (iCCR), enabling fast, accurate, and real-time face tracking with efficient online model updates.

Findings

iCCR is an order of magnitude faster than standard methods.

iCCR achieves state-of-the-art tracking performance.

The method enables real-time face tracking with online model updates.

Abstract

This paper introduces a novel real-time algorithm for facial landmark tracking. Compared to detection, tracking has both additional challenges and opportunities. Arguably the most important aspect in this domain is updating a tracker's models as tracking progresses, also known as incremental (face) tracking. While this should result in more accurate localisation, how to do this online and in real time without causing a tracker to drift is still an important open research question. We address this question in the cascaded regression framework, the state-of-the-art approach for facial landmark localisation. Because incremental learning for cascaded regression is costly, we propose a much more efficient yet equally accurate alternative using continuous regression. More specifically, we first propose cascaded continuous regression (CCR) and show its accuracy is equivalent to the Supervised Descent Method. We then derive the incremental learning updates for CCR (iCCR) and show that it is an order of magnitude faster than standard incremental learning for cascaded regression, bringing the time required for the update from seconds down to a fraction of a second, thus enabling real-time tracking. Finally, we evaluate iCCR and show the importance of incremental learning in achieving state-of-the-art performance. Code for our iCCR is available from http://www.cs.nott.ac.uk/~psxes1