Facial feature detection and tracking with automatic template selection

Abstract

We describe an accurate and robust method of locating facial features. The method utilises a set of feature templates in conjunction with a shape constrained search technique. The current feature templates are correlated with the target image to generate a set of response surfaces. The parameters of a statistical shape model are optimised to maximise the sum of responses. Given the new feature locations the feature templates are updated using a nearest neighbour approach to select likely feature templates from the training set. We find that this template selection tracker (TST) method outperforms previous approaches using fixed template feature detectors. It gives results similar to the more complex active appearance model (AAM) algorithm on two publicly available static image sets and outperforms the AAM on a more challenging set of in-car face sequences