Conference Paper

Hand Gesture Recognition for Human-Machine Interaction.

Source: DBLP

ABSTRACT Even after more than two decades of input devices development, many people still find the interaction with computers an uncomfortable experience. Efforts should be made to adapt computers to our natural means of communication: speech and body language. The PUI paradigm has emerged as a post-WIMP interface paradigm in order to cover these preferences. The aim of this paper is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB web cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The most important part of the recognition process is a robust shape comparison carried out through a Hausdorff distance approach, which operates on edge maps. The use of a visual memory allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. This paper includes experimental evaluations of the recognition process of 26 hand postures and it discusses the results. Experiments show that the system can achieve a 90% recognition average rate and is suitable for real-time applications.

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we address the problem of dynamic trajectory segmentation for human-computer interfaces. We are concerned with locally linear trajectories. Trajectory points are obtained from a hand feature detector. First, a tensor voting technique is used to filter the trajectory and to construct a smooth trajectory from the sparse collection of detected points. The tensor voting scheme is also in accordance with perceptual principles. Local linearity of the trajectory permits us to have a decision based on an analysis of the corresponding modes in the Radon space. A mode detector in this space allows us to find the orientation of each trajectory segment. The entire trajectory is encoded by a sequence of directions, thus, allowing a large number of possible meaningful gestures to be defined in the HCI.
    Applied Computational Intelligence and Informatics (SACI), 2011 6th IEEE International Symposium on; 06/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.
    RO-MAN, 2011 IEEE; 09/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Gesture recognition techniques are used in order to achieve spontaneous and natural machine interactions. Normal Hand gesture based recognition techniques using single 2D camera have serious issues in terms of correct tracking; also the false recognition occurrences are higher. In this paper, we present an efficient method of implementing gesture recognition using marker. In this approach we use a single marker for gesture recognition by doing mouse emulation. Tracking is accurate and false recognition occurrences are found to be very low in this method. Also this approach is found to be computationally efficient and better user experience compared to other hand gesture recognition techniques using single 2D camera.
    International Journal of Computer Applications. 11/2013; 81(1):11-15.


Available from