Using Confusion Reject to Improve (User and) System (Cross) Learning of Gesture Commands

Abstract

This paper presents a new method to help users defining personalized gesture commands (on pen-based devices) that maximize recognition performance from the classifier. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from scratch or very few data samples and that will learn incrementally to achieve good performance after some using time. Our objective is to make the user aware of the recognizer difficulties during the definition of commands, by detecting confusion among gesture classes, in order to help him define a gesture set that yield good recognition performance from the beginning. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A realistic experiment has been made on 55 persons to validate our confusion detection technique, and it shows that our method leads to a significant improvement of the classifier recognition performance.