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We are trying to realize gesture recognition from motion images that capture human gestures and hand motions without the need for tactile sensors or markers, such as data gloves. We have achieved real-time spotting-based recognition with the continuous DP system without any gesture performers having to be aware of the commencement and ending of a motion. Continuous DP is beneficial due to its ability to create a model from a single-gesture motion image sequence. Therefore, even if gesture motions depend on performers, gestures can be recognized after a single instruction. However, to instruct a model, our conventional real-time recognition systems require setting of the threshold for recognition as well as segmentation of the model off-line. Therefore, we propose in this paper an automatic model segmentation method and a recognition method to allow constructing performer-adaptive on-line teaching systems. We will also demonstrate the usefulness of this method through evaluative experiments. © 1999 Scripta Technica, Syst Comp Jpn, 31(1): 3947, 2000

Adaptation to gesture performers by an on-line teaching system for spotting recognition of gestures from a time-varying image