Hand gesture recognition using subunit-based dynamic time warping

A subunit-based Dynamic Time Warping (DTW) approach is introduced for hand movement recognition. Two major contributions distinguish the proposed approach from conventional DTW. (1) A set of hand movement subunits is constructed using a data-driven method. The learning is based on subunits instead of the whole hand movement for more efficient learning. (2) A more accurate similarity measure is offered using subunit-to-subunit matching to absorb the difference between two similar sub-sequences belonging to the same subunit, and only keeping the distances between sub-sequences that relate to different subunits. Compared with the conventional DTW approach, the proposed approach is experimentally demonstrated to be both accurate and efficient for locally collected datasets.