Image Processing based Human Gesture Recognition using Kinect Camera

This paper introduces a novel way of the depth map and RGB image alignment for Kinect. Rule base algorithm is employed to estimate the joint co-ordinates from both depth and RGB images, and this technique is robust for the uncertainties of the particular extracted joint co-ordinates. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. The experimental results demonstrated which the proposed method is competent at precisely aligning the particular depth and RGB images, contributes a excellent improvement for body gesture segmentation, and may potentially improve the particular performance of body gesture recognition in Human-Machine Interaction (HMI). KeywordsKinect Camera, Matlab, Hand Gesture, RGB and Depth data, Image Processing I.INTRODUCTION The research on HMI is to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans. Among different human body parts, the hand is the most effective general-purpose interaction tool, due to its dexterity. Adopting hand gesture as an interface in HMI will not only allow the deployment of a wide range of applications in sophisticated computing environments such as virtual reality systems and interactive gaming platforms, but also benefit our daily life such as providing aids for the hearing impaired, and maintaining absolute sterility in health care environment using touchless interfaces via gestures [1]. Despite a significant research effort in the last decade, recognizing human actions from motion is still a challenging problem on account of scene complexity (e.g., occlusions, clutter, interacting objects, illumination changes), acquisition difficulties (camera movement, viewpoint changes), and the complexity of human actions (non-rigid objects, action variability). At the moment, the most beneficial tools for capturing hands gesture usually are electro-mechanical or even magnetic realizing devices (data gloves) [2]. These procedures employ sensors attached to a glove that transduces finger flexions into electrical alerts to determine the hands gesture. They deliver essentially the most complete, application-independent pair of real-time measurements from the hand with HMI. Nonetheless, they include several negatives (i) they are expensive for laidback use, (ii) they hinder the naturalness of hand gesture, and (iii) needed complex calibration and startup procedures for getting precise proportions. Vision-based hands gesture identification serves being a promising alternative to them for the potential to provide more natural, unencumbered, non-contact conversation. However, despite a lot of previous work [3], [5], [6], regular vision-based hands gesture identification methods continue to be far by satisfactory pertaining to real-life applications. In Nov 2010 Microsoft launched Kinect, an infraredlight range-sensing camera, and in the process added raw depth being a new video-data get modality. Throughout June 2011 Microsoft released a Kinect Software package Development Equipment (SDK) consisting of a list of powerful algorithms pertaining to extracting landscape depth and object masks, and subsequently creating a skeleton type of a person in front of the camera in real time. Although Kinect’s concentrate on application is video gaming, the 3D get and robustness associated with Kinect as well as SDK get helped spawn numerous research and “hacking” projects in the field associated with humanMachine interfaces. Most approaches, nevertheless, rely upon tracking not really recognition. Usually a collection of parameters, e. g. , thresholds upon joint areas, velocities and accelerations, are generally specified to localize and track motions. In this paper,we propose a Novel hand gesture recognition algorithm called Rule Base Algorithm to detect the location and orientation of an object in three-dimensional space ,real time using MATLAB and the X-box Kinect camera. The solution employed uses the MATLAB Image Acquisition toolbox and Image Processing Toolbox to retrieve and analyze the Kinect’s RGB and Depth data. The system authenticates & actuates by means of several procedural actions such as abstract formation for human body & necessary actions to be taken out to predict the skeleton, point generation based on 3d posture based point value of an image predicted for human body skeleton, segmentation of human body,generating value for each segmentation and generating code for the value obtained from the predicted posture. International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert. org ICESMART-2015 Conference Proceedings Volume 3, Issue 19 Special Issue 2015