Real time hand gesture recognition via finger-emphasized multi-scale description
The development of depth cameras, e.g., the Kinect sensor, provides new opportunities for human computer interaction (HCI). Although the Kinect sensor has been extensively applied for human tracking, human action recognition and hand gesture recognition, real time hand gesture recognition is still a challenging problem. In this paper, we propose a new real time hand gesture recognition method. To represent the noisy and articulated hand shape segmented from the Kinect images, a finger emphasized multi-scale descriptor is proposed. To fully utilize hand shape features, this descriptor incorporates three types of parameters of multiple scales, which emphasize the finger features. Hand gesture recognition is then achieved with both DTW algorithm and BP neural network. Extensive experimental results and the comparison with state-of-the-art methods demonstrate that our method is accurate (a 100% accuracy on a challenging hand gesture dataset), efficient (average 0.941ms per frame), and robust to noise, articulations and rigid transformations.