Enhanced feature extraction method for hand gesture recognition using support vector machine

In this paper, a method is proposed to maximize the accuracy during the feature extraction stage in a real time system for hand gesture recognition by escalating the number of parameters of the feature set for support vector machine. Numerous former researches utilized hu moments but they didn´t correspond to the complete description of an image, and was suitable only for giving very rough estimation of possible match. Thus matching performance was not acceptable for image retrieval. On the other hand, the accuracy of the support vector machine (SVM) depends on the number of support vectors. Hence adding features that significantly improve the splitting probability of training images decrease the number of support vectors and improves the performance of the SVM. Therefore to enhance the harmonizing of images, together with hu moments, edge histogram descriptor and circularity shape parameter is used to compose the feature vector. Experiments on series of test images show that the proposed method yields better matching performance. Integrated feature based approach to hand gesture recognition has been tested over 23 gestures and it gave promising results.