Hand Gesture Recognition using Principal Component Analysis

Authors(3) :-Sunil Kumar, Tanu Srivastava, Raj Shree Singh

Nowadays actions are increasingly being handled in electronic ways, instead of physical interaction. From earlier times biometrics is used in the authentication of a person. It recognizes a person by using a human trait associated with it like eyes (by calculating the distance between the eyes) and using hand gestures, fingerprint detection, face detection etc. Advantages of using these traits for identification are that they uniquely identify a person and cannot be forgotten or lost. These are unique features of a human being which are being used widely to make the human life simpler. Hand gesture recognition system is a powerful tool that supports efficient interaction between the user and the computer. The main moto of hand gesture recognition research is to create a system which can recognise specific hand gestures and use them to convey useful information for device control. This paper presents an experimental study over the feasibility of principal component analysis in hand gesture recognition system. PCA is a powerful tool for analyzing data. The primary goal of PCA is dimensionality reduction. Frames are extracted from the Sheffield KInect Gesture (SKIG) dataset. The implementation is done by creating a training set and then training the recognizer. It uses Eigen space by processing the eigenvalues and eigenvectors of the images in training set. Euclidean distance with the threshold value is used as similarity metric to recognize the gestures. The experimental results show that PCA is feasible to be used for hand gesture recognition system.

Authors and Affiliations

Sunil Kumar
Department of Computer Science and Engineering, Mody University of Science and Technology Lakshmangarh, Rajasthan, India
Tanu Srivastava
Department of Computer Science and Engineering, Mody University of Science and Technology Lakshmangarh, Rajasthan, India
Raj Shree Singh
Department of Computer Science and Engineering, Mody University of Science and Technology Lakshmangarh, Rajasthan, India

Hand Gesture Recognition, Eigen Values And Eigen Vectors, Principal Component Analysis (PCA), Human Computer Interaction (HCI).

  1. Rama B. Dan, P. S. Mohod "Survey on Hand Gesture Recognition Approaches," International Journal of Computer Science and Information Technologies, 5 (2), 2014, 2050-2052
  2. Mule, K. C., & Holambe, A. N. "Hand Gesture Recognition Using PCA and Histogram Projection,” International Journal on Advanced Computer Theory and Engineering (IJACTE),2(2),2013, 74-78.
  3. Shitole, S. M., Patil, S. B., & Narote, S. P. "Dynamic hand gesture recognition using PCA, Pruning and ANN,”  International Journal of Computer Applications, 74(2),2013,  24-29.
  4. Agrawal, T., & Chaudhuri, S. " Gesture recognition using position and appearance features,” Image Processing, 3, 2003,  109-112.
  5. Jeong, D. H. and Ziemkiewicz, C. and Ribarsky, W. and Chang, " Understanding Principal Component Analysis Using a Visual Analytics Tool,” Charlotte Visualization Center, UNC Charlotte, 2009.

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 886-890
Manuscript Number : CSEIT1724215
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sunil Kumar, Tanu Srivastava, Raj Shree Singh , "Hand Gesture Recognition using Principal Component Analysis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.886-890, July-August.2017
URL : http://ijsrcseit.com/CSEIT1724215

Follow Us

Contact Us