Date on Master's Thesis/Doctoral Dissertation


Document Type

Master's Thesis

Degree Name



Computer Engineering and Computer Science

Committee Chair

Ouyang, Ming

Author's Keywords

Human-computer interaction; Finger identification; Hand gesture recognition; Kinect


Human-computer interaction; Computer vision; Human-machine systems; Nonverbal communication


Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this thesis. a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HCR system is implemented with Microsoft Visual Studio 2010. Two different scenarios for HGR are provided: the Popular Gesture with nine gestures, and the Numbers with nine gestures. The system allows the users to select a scenario, and it is able to detect hand gestures made by users. to identify fingers, and to recognize the meanings of gestures, and to display the meanings and pictures on screen. The accuracy of the HGR system is from 84% to 99% with single hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions. signers' skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life.