A Fast Detection and Recognition Algorithm with Construction of Fast Support Vector Machine based on Entropy Weight

Abstract

In gesture recognition, gesture is easy to be influenced by nonlinear factors such as illumination, wrists and motion blur, etc., based on the Analysis of the SVM classification method and its effect in practical application, this paper proposes a gesture method based on the combination of nonlinear support vector machine (SVM) and Linear Discriminant Analysis, LDA (SVM+LDA).First it works out gesture recognition system constituted by multiple-surface electrode sensor, microcontroller acquisition unit, and computer; second, to get the best classification feature of image through linear discriminant analysis with LDA feature extraction; finally, to carry out classification recognition on gesture characteristic vector by applying the nearest neighbor classifier, at the same time compared with other classification algorithms to get the advantages and disadvantages of the algorithm in this paper. The experimental results show that the SVM+LDA can gain higher recognition rate, so as to provide theoretical and data support for the researcher of electromyogram artificial hands control to choose the appropriate means of recognition.