Developing a new automated model to classify combined and basic gestures from complex head motion in real time by using All-vs-All HMM

Human head gestures convey a rich message, containing information deliver for peoples as a communication tool. Nodding, shacking are commonly used gestures as non-verbal signals to communicate their intent and emotions. However, the majority of head gestures classification systems focused on head nodding and shaking detection. while they ignored other head gestures which have more expressive emotional signals like rest(up and down), turn, tilt, and tilting. In this paper we developed a new model to classify all head gestures (rest, turn, tilt, node, shake, and tilting) from complex head motions. The model methodology based on distinguishing basic head movements (rest, turn, and tilt) and combined movements (nodding, shaking, and tilting). The purpose of this system is to detect and label combined and basic head movements in dynamic video. In addition, this phase of this study looking at developing an affective machine uses head movements to extract complex affective states (this work is underway). The system used 3D head rotation angles to classify relevant head gestures in-plan and out-plan of view during user interaction with computer. This system used an open source tracker to detect and track head movements. The Three angels that obtained from the tracker (pitch, yaw, and roll), were analyzed and packed into sequences of observation symbols or cues. Observations formed inputs to an all-vs-all discrete Hidden Markov Model (HMM) classifier. Three classifiers were used for each angle. The classifiers are trained on Boston University dataset, and tested on available mind reading data. The system evaluate on video streams in real time by webcam. The system is fully automatic without incurring any cost of technical methods and doesnג€™t require any sensitive tools.