Hand Tracking and Hand Gesture Recognition for Human Computer Interaction

By Gope, Dejan Chandra | American Academic & Scholarly Research Journal, November 2012 | Go to article overview

Hand Tracking and Hand Gesture Recognition for Human Computer Interaction


Gope, Dejan Chandra, American Academic & Scholarly Research Journal


ABSTRACT: Hand tracking and hand gesture recognition is an important problem in the field of human-computer interaction. A number of solutions have been proposed in the current literature, but the problem is still far from being solved since the hand exhibits significant amounts of articulation and self-occlusion that cause difficulties with existing algorithms. To further exasperate these problems, interactive applications require that the hand tracking perform in real-time. The current ubiquity of webcams offers an opportunity to create computer vision systems which can enable novel new methods for human-computer interaction. To that end, we present a system which allows the user to control the operating system cursor in a hands-free way by gesturing in mid-air. Our system leverages OpenCV and the X windowing system to track the index finger and thumb of a user using a webcam. The user can motion in the direction she/he wishes the cursor to move, and can execute mouse operations by "pointing" toward the camera and breaking a "plane of interaction". Our goal with this work is to demonstrate a proof-of-concept system for enabling a new method of controlling and interfacing with a computer using commodity webcams.

Keywords: Hand Gesture Recognition, Hand Pointing, Pointing Accuracy, Computer Vision, Fingertip Interaction, Human Computer Interaction(HCI), Hand-posture Recognition, Hand Gesture Pose Estimation.

1 INTRODUCTION

Vision-based hand gesture recognition is an active area of research in human-computer interaction (HCI), as direct use of hands is a natural means for humans to communicate with each other and more recently, with devices in intelligent environments. The trend in HCI is moving towards real-time hand gesture recognition and tracking for use in interacting with video games, remote-less control of television sets, and interacting with other similar environments. Given the ubiquity of mobile devices such as smart phones and notebooks with embedded cameras, a hand gesture recognition system can serve as an important way of using these camera-enabled devices to interact more intuitively than traditional interfaces. This paper presents the implementation and analysis of a vision-based static hand pose estimation technique. The system uses a single low-resolution camera (640x480, 1067x800 - mobile phone and notebook web cameras) under uniform lighting conditions, and estimates the 2D orientation or the pose of the hand gesture. In interactive applications such as volume control of a music player, a 2D navigation game, multimedia browsing, the orientation of a single pointing gesture or a directed palm can be used to manipulate the various levels of output.

2 RELATED WORK

Hand gesture recognition and tracking has been an important and active area of research in the field of HCI, and sign language recognition. The use of glove-based devices to measure hand location and shape, especially for virtual reality, has been actively studied. In spite of achieving high accuracy and speed in measuring hand postures, this approach is not suitable for certain applications due to the restricted hand motion caused by the attached cables. Computer vision techniques measure hand postures and locations from a distance, providing for unrestricted movement. Numerous approaches have been explored by the vision community to extract human skin regions either by background subtraction or skin-color segmentation. Methods based on background subtraction are not feasible when applied to image with complex Vision-Based Hand Gesture Pose Estimation for Mobile Devices backgrounds or real-world scenarios where the user wants to use the application on-the-go. Once the image regions are identified by the system, the image regions can be analyzed to estimate the hand posture. Specifically, for finger gesture recognition and tracking, a common approach is to extract hand regions and then locate the fingertip to determine the pose orientation. …

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