Hand Gesture Recognition for Human-Computer Interaction

Abstract

Problem statement: With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands can be used as an input device for providing natural interaction. Approach: In this study, Gaussian Mixture Model (GMM) was used to extract hand from the video sequence. Extreme points were extracted from the segmented hand using star skeletonization and recognition was performed by distance signature. Results: The proposed method was tested on the dataset captured in the closed environment with the assumption that the user should be in the Field Of View (FOV). This study was performed for 5 different datasets in varying lighting conditions. Conclusion: This study specifically proposed a real time vision system for hand gesture based computer interaction to control an event like navigation of slides in Power Point Presentation.