Determining a Number of Training Data for Gesture Recognition Considering Decay in Gesture Movements

Abstract

Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as those in drawing objects. Gesture recognition systems generally require several samples of training data before recognition takes place. However, recognition accuracy deteriorates as time passes since the trajectory of the gestures changes due to fatigue or forgetfulness. We investigated the change in gestures and found that the first several samples of gestures were not suitable for training data. Therefore, we propose two methods of finding appropriate data for training for long-term use. We confirmed that the proposed methods found better training data than the conventional method from the viewpoints of the number of data collected and recognition accuracy.