Colour invariant head pose classification in low resolution video

Abstract

This paper presents an algorithm for the classification of head pose in low resolution video. Invariance to skin, hair and background colours is achieved by classifying using an ensemble of randomised ferns which have been trained on labelled images. The ferns are used to simultaneously classify the head pose and to identify the most likely hypothesis for the mapping between colours and labels. Results from video sequences demonstrate that an improved posterior estimation using learnt colour distributions reduces classification error and provides accurate pose information in images where the head occupies as little as 10 pixels square.