Depth matrix and adaptive Bayes classifier based dynamic hand gesture recognition

Abstract A sequence of apparently ad-hoc hand postures can generate meaningful dynamic gestures which can be utilized in interface controls for computer, television, or games. In order to develop deployable systems with these gestures, selected descriptors should be fast enough to meet the live recognition requirements. This paper proposes framework for a practical system capable of recognizing continuous dynamic gestures characterized by short-duration posture sequences. A depth-based modification to the shape matrix is devised to describe hand silhouettes, which gives a faster alternative to region-based descriptors. Postures are recognized using depth matrix and 1-nearest neighbor strategy. Posture sequence labels are predicted by a dynamic naive Bayes classifier which works in association with an adaptive windowing mechanism. The conducted experiments report up to 96.2% accurate results with mean accuracy of 95.2% on dynamic gesture dataset. Depth matrix computation takes a maximum of 2ms time.