An Application of Classifier Combination Methods in Hand Gesture Recognition

Abstract EN

Hand gesture recognition is a topic in artificial intelligence and computer vision with the goal toautomatically interpret human hand gestures via some algorithms.

Notice that it is a difficult classificationtask for which only one simple classifier cannot achieve satisfactory performance; several classifiercombination techniques are employed in this paper to handle this specific problem.

Based on some relateddata at hand, AdaBoost and rotation forest are seen to behave significantly better than all the otherconsidered algorithms, especially a classification tree.

By investigating the bias-variance decompositionsof error for all the compared algorithms, the success of AdaBoost and rotation forest can be attributedto the fact that each of them simultaneously reduces the bias and variance terms of a SingleTree's errorto a large extent.

Meanwhile, kappa-error diagrams are utilized to study the diversity-accuracy patternsof the constructed ensemble classifiers in a visual manner.