Head tracking with combined face and nose detection

Abstract

We present a facial feature detector for time-of-flight (TOF) cameras that extends previous work by combining a nose detector based on geometric features with a face detector. The goal is to prevent false detections outside the area of the face. To detect the nose in the image, we first compute the geometric features per pixel. We then augment these geometric features with two additional features: The horizontal and vertical distance to the most likely face detected by a cascade-of-boosted-ensembles face detector. We use a very simple classifier based on an axis-aligned bounding box in feature space; pixels whose feature values fall within the box are classified as nose pixels, and all other pixels are classified as “non-nose”. The extent of the bounding box is learned on a labeled training set. Despite its simiplicity, this detector already delivers satisfactory results on the geometric features alone; adding the face detector improves the equal error rate (EER) from 22.2% (without face detector) to 10.4% (with face detector). (Note when comparing with our previous results from [1] and [2] that, in contrast to this paper, the test data used there did not contain scale variations.)