Recognition of static hand gestures using support vector machines - Basa, Tristan M. (2000)
Support Vector Machines (SVM) are a class of new learning algorithms that is currently gaining widespread attention in the field of machine learning and computer vision. Recent researches have concluded that SVM have significantly better performance in applications mostly dominated by learning techniques. In this paper, we investigate the performance of SVMs in the recognition of hand gestures, particularly that of static gestures of the American Sign Language. Experimental results show that the biggest factor affecting the performance of an appearance-based hand gesture system using SVM without complicated feature extraction is the training method. For high recognition accuracies an enrollment system is suggested. We also examined the effects of image size, type of kernel used, and classification scheme on recognition accuracy, training speed, and recognition speed.