Vision-based gesture recognition system for human-computer interaction

Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research, is to create a system, which can identify specific gestures and use them to convey information or for device control. For that, gestures need to be modelled in the spatial and temporal domains, where a hand posture is the static structure of the hand and a gesture is the dynamic movement of the hand. Being hand-pose one of the most important communication tools in human’s daily life, and with the continuous advances of image and video processing techniques, research on human-machine interaction through gesture recognition led to the use of such technology in a very broad range of applications, like touch screens, video game consoles, virtual reality, medical applications, etc. There are areas where this trend is an asset, as for example in the application of these technologies in interfaces that can help people with physical disabilities, or areas where it is a complement to the normal way of communicating. There are basically two types of approaches for hand gesture recognition: vision-based approaches and data glove methods. In the study we will be focusing our attention on vision-based approaches. Why vision-based hand gesture recognition systems? Vision-based hand gesture recognition systems provide a simpler and more intuitive way of communication between a human and a computer. Using visual input in this context makes it possible to communicate remotely with computerized equipment, without the need for physical contact. The main objective of this work is to study and implement solutions that can 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. In pursuit of this, we intend to use a depth sensor camera to detect and extract hand information (hand features), for gesture classification. With the implemented solutions we intend to develop an integrated vision-based hand gesture recognition system, for offline training of static and dynamic hand gestures, in order to create ABSTRACT: Hand gesture recognition, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. This work intends to study and implement a solution, generic enough, able to interpret user commands, composed of a set of dynamic and static gestures, and use those solutions to build an application able to work in a realtime human-computer interaction systems. The proposed solution is composed of two modules controlled by a FSM (Finite State Machine): a real time hand tracking and feature extraction system, supported by a SVM (Support Vector Machine) model for static hand posture classification and a set of HMMs (Hidden Markov Models) for dynamic single stroke hand gesture recognition. The experimental results showed that the system works very reliably, being able to recognize the set of defined commands in real-time. The SVM model for hand posture classification, trained with the selected hand features, achieved an accuracy of 99,2%. The proposed solution as the advantage of being computationally simple to train and use, and at the same time generic enough, allowing its application in any robot/system command interface.