An efficient algorithm for 3D hand gesture recognition using combined neural classifiers
Gestures are the dynamic movements of hands within a certain time interval, which are of practical importance in many areas, such as humancomputer interaction, computer vision, and computer graphics. The human hand gesture can provide a free and natural alternative to todays cumbersome interface devices so as to improve the efficiency and effectiveness of humancomputer interaction. This paper presents a neural-based combined classifier for 3D gesture recognition. The combined classifier is based on varying the parameters related to both the design and training of neural network classifier. The boosting algorithm is used to make perturbation of the training set employing the Multi-Layer Perceptron as base classifier. The final decision of the ensemble of classifiers is based on the majority voting rule. Experiments performed on 3D gesture database show the robustness of the proposed technique.
gesture recognition combined classifier artificial neural networks adaboost committee machine