Face tracking using a combination of colour and pattern matching based on particle filter

Abstract

Robust real-time face tracking is an important and challenging task in computer vision applications. In this paper, we propose a novel particle filter algorithm to robustly track faces. Particle observations are computed by considering cue and appearance feature. Cue feature is used to identify skin regions, e.i. face and hands, while appearance is used to directly label targets. Normalized Cross Correlation (NCC) between an image template and particle samples is computed to robustly find the face among other skin regions. In other words, the image template is registered to a frame using particle filter to perform the optimization. Real-time is achieved by using integral images to compute image features. Evaluation results show the advantages and limitation of our approach.