Author Description

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices. Hand gesture recognition is a difficult problem and the current work is only a small contribution towards achieving the results needed in the field. The main objective of this work was to study and implement solutions that could be generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for online gesture recognition. To achieve this, a set of implementations for processing and retrieving hand user information, learn statistical models and able to do online classification were created. The final prototype is a generic solution able to interpret static and dynamic gestures and that can be integrated with any human robot/system interface. The implemented solution, is easily configured to learn different static and dynamic gestures, while creating statistical models that can be used in any real-time user interface for online gesture classification.