Head gesture recognition using HMMs

Abstract

This paper addresses a technique of recognizing a head gesture. The proposed system is composed of eye tracking and head motion decision. The eye tracking step is divided into face detection and eye location. Face detection obtains the face region using neural network and mosaic image representation. Eye location extracts the location of eyes from the detected face region. Eye location is performed in the region close to a pair of eyes for real-time eye tracking. If a pair of eyes is not located, face detection is performed again. After eye tracking is performed, the coordinates of the detected eye are transformed into the normalized vector of the x-coordinate and the y-coordinate. Three methods are tested for head motion decision: head gesture recognition with direct observation, head gesture recognition using two Hidden Markov Models (HMMs) and head gesture recognition using three HMMs. Head gesture can be recognized by direct observation of the variation of the vector, but the variation of the vector can be observed by two HMMs for more accurate recognition. However, because this method doesn't recognize neutral head gesture, three HMMs learned by a directional vector is adopted. The directional vector represents the direction of head movement. The vector is inputted into HMMs to determine neutral gesture as well as positive and negative gesture. Combined head gesture recognition using above three methods is also discussed. The experimental results are reported.