Visual Recognition of Hand Postures for Interacting with Virtual Environments

The paper addresses the problem of visual recognition of several hand postures corresponding to a few operations commonly performed in virtual environments, such as: object selection, translation, rotation and resizing. Processing is performed in a top-view scenario with a top-mounted camera that monitors the user’s hands on the working desktop. By careful choosing and controlling of the scene and lighting conditions, hands segmentation is fast and robust which increases the performances of the hand posture classifier. The chosen classifier was a multilayered perceptron with three layers. By keeping all the processing at a low level of complexity and by considering an appropriate control of the environment, we obtain a real time 25 fps functional system with high detection and recognition accuracy results.