Dynamic Hand Gesture Recognition Using Depth Data

摘要

The subject disclosure is directed towards a technology by which dynamic hand gestures are recognized by processing depth data, including in real-time. In an offline stage, a classifier is trained from feature values extracted from frames of depth data that are associated with intended hand gestures. In an online stage, a feature extractor extracts feature values from sensed depth data that corresponds to an unknown hand gesture. These feature values are input to the classifier as a feature vector to receive a recognition result of the unknown hand gesture. The technology may be used in real time, and may be robust to variations in lighting, hand orientation, and the user's gesturing speed and style.