Localising facial features with matched filters

Abstract

This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. The matched filters are extracted from training images using inverse Fourier analysis. We provide an experimental evaluation of the method on the University of Berne face data-base. Here we explore the most effective choice of training data so that the filters can be effectively applied when the facial pose varies. We also evaluate the effectiveness of the method when facial occlusion due to spectacles is present.