Novel Method for using Hand Recognition as Computer Remote Control with Computer Vision Techniques
Keywords:Segmentation by HSV, Hand Geometry, Gesture Recognition, Remote Control, HCI,
AbstractToday, interaction between man-computer (HCI) is one of the most prominent goals. One of the important goals is to develop an independent control of external devices or static controls over a computer for simplified system and userfriendly interface. Detection and recognition of gestural parts of a person's hand plays a crucial role because it is used to perform almost all of the daily activities. This work is aimed at facilitating the way of exercising control over the PC using C++ programming language via machine vision libraries like OpenCV. The segmentation of the hand was performed in two stages: the first stage used a range of color with the HSV model, accompanied by morphological operations to minimize noise. The second stage, which was conducted after binarization, continued to seek the contour of the hand focusing on the most important features of its geometry. A convex hull and convexity defects were set to determine the type of gesture and assign a particular function to run on the computer. The center of the mass of the contour of the hand was located to obtain its coordinates (x, y). It was subsequently assigned to the mouse position connected to the PC to emulate the 2D scrolling on the screen, consistent to the movement of the hand. In comparison to the traditional input devices, this approach facilitated a convenient manipulation of computer tools, providing a greater control and user comfort.
Yue Z., Xin X., Chao C. and Dan Y.. 2013. Color Image
Segmentation Algorithm of Rapid Level Sets Based on HSV Color Space. Springer Link. 483-489.
Bonato V., Fernandes M. M. and Marques E.. 2007. A Smart Camera with Gesture Recognition and SLAM Capabilities for Mobile Robots. Taylor & Francis.
Schröder, M., Jonathan M., Ritter H. and Botsch M. 2014. Real-time Hand Tracking using Synergistic Inverse Kinematics. IEEE, 5447 -5454.
Halim, Z., & Abbas, G. 2014. A Kinect-based Sign Language Hand
Gesture Recognition System for Haring- and Speech- Impaired:
Pilot Study of Pakistani Sign Language. Taylor & Francis.
Omar Al-Jarrah and Faruq A. Al-Omari. 2007. Improving Gesture Recognition in the Arabic sign Language using Texture Analysis.
Zhong, H., Juan P W., and Shimon Y. N. 2013. A Collaborative Telerobotics Network Framework with Hand Gesture Interface and Conflict Prevention. International Journal of Production Research 51(15): 4443-4463.
Karbasi, M., Bhatti, Z., Aghababaeyan, R., Bilal, S., Rad, A. E., Shah, A., & Waqas, A. 2016. Real-Time Hand Detection by Depth Images: A Survey. Jurnal Teknologi, 78(2).
Haitham Sabah Badi and Sabah Hussein. 2014. Hand Posture and Gesture Recognition Technology. Springer Link.
B. Burger, I Ferrané, F. Lerasle and G. Infantes. 2011. Two-handed Gesture Recognition and Fusion with Speech to Command a Robot. Springer Link.
Wachs, J. P., Stern, H., Edan, Y. 2005. Cluster Labeling and Parameter Estimation for the Automated Setupof a Hand-gesture Recognition System. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35(6):932-944.
James P. M., Subhasis C. and Tushar A. 2015. Hierarchical
Recognition of Dynamic Hand Gestures for Telerobotic Application. Taylor & Francis.
Chi-M. P., Hong-M. Z. and Wei F. 2012. Real-Time Hand Gesture Recognition using Motion Tracking. Taylor & Francis.
Zabri Abu Bakar, M., Samad, R., Pebrianti, D. and L. Y. A. Nicolaas. 2014. Real-time Rotation Invariant Hand Tracking using 3D data.IEEE. 490 - 495.
Huasong Huang, Yulong Zhou, Pengjin Chen and Runwei Ding. 2014. Robust Hand Tracking with Posture Recognition via online learning. IEEE. 65 - 70.
Bhuyan, M. K., Ghosh D. and Bora P. K. 2007. Hand motion tracking and trajectory matching for dynamic hand gesture recognition. Taylor & Francis.
Osamu Ikeda. 2003. Segmentation of Faces in Video Footage using Controlled Weights on HSV Color. Springer Link. 163-170.
Nguyen, T. N., Vo, D. H. Huynh H. H., and Meunier J. Geometrybased Static Hand Gesture Recognition Using Support Vector Machine. IEEE. 769 - 774.
Zheng, G., Wang C. J. and Boult T. E. Application of Projective Invariants in Hand Geometry Biometrics. IEEE. 758 - 768.
Zhang, X. N., Jiang, J., Liang Z. H., and Liu C. L. Skin Color Enhancement Based on Favorite Skin Color in HSV Color Space. IEEE. 1789 - 1793.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.