Vision-Based Human Gesture Recognition Using Kinect Sensor

Gestures are indeed important in our daily life as they serve as one of the communication platform by using body motions in order to deliver information or effectively interact. This paper proposes to leverage the Kinect sensor for close-range human gesture recognition. The orientation details of human arms are extracted from the skeleton map sequences in order to form a bag of quaternions feature vectors. After the conversion to log-covariance matrix, the system is trained and the gestures are classified by multi-class SVM classifier. An experimental dataset of skeleton map sequences for 5 subjects with 6 gestures was collected and tested. The proposed system obtained remarkably accurate result with nearly 99 % of average correct classification rate (ACCR) compared to state of the art method with ACCR of 95 %.