Abstract: A novel method for video-based head gesture recognition using eye information by an eye tracker has been proposed. The method uses a combination of gaze and eye movement to infer head gestures. Compared to other gesture-based methods a major advantage of the method is that the user keeps the gaze on the interaction object while interacting. This method has been implemented on a head-mounted eye tracker for detecting a set of predefined head gestures. The accuracy of the gesture classifier is evaluated and verified for gaze-based interaction in applications intended for both large public displays and small mobile phone screens. The user study shows that the method detects a set of defined gestures reliably.