Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 346951, 17 pages
Research Article

An Application of Classifier Combination Methods in Hand Gesture Recognition

1School of Mechanical Engineering, Xi'an Jiaotong University, Shaanxi, Xi'an, 710049, China
2School of Mathematics and Statistics, Xi'an Jiaotong University, Shaanxi, Xi'an, 710049, China

Received 5 May 2011; Revised 27 September 2011; Accepted 9 October 2011

Academic Editor: Gordon Huang

Copyright © 2012 Guan-Wei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hand gesture recognition is a topic in artificial intelligence and computer vision with the goal to automatically interpret human hand gestures via some algorithms. Notice that it is a difficult classification task for which only one simple classifier cannot achieve satisfactory performance; several classifier combination techniques are employed in this paper to handle this specific problem. Based on some related data at hand, AdaBoost and rotation forest are seen to behave significantly better than all the other considered algorithms, especially a classification tree. By investigating the bias-variance decompositions of error for all the compared algorithms, the success of AdaBoost and rotation forest can be attributed to the fact that each of them simultaneously reduces the bias and variance terms of a SingleTree's error to a large extent. Meanwhile, kappa-error diagrams are utilized to study the diversity-accuracy patterns of the constructed ensemble classifiers in a visual manner.