Hand posture recognition with co-training
As an emerging human-computer interaction approach vision based hand interaction is more natural and efficient. However in order to achieve high accuracy, most of the existing hand posture recognition methods need a large number of labeled samples which is expensive or unavailable in practice. In this paper, a co-training based method is proposed to recognize different hand postures with a small quantity of labeled data. Hand postures examples are represented with different features and disparate classifiers are trained simultaneously with labeled data. Then the semi-supervised learning treats each new posture as unlabeled data and updates the classifiers in a co-training framework. Experiments show that the proposed method outperforms the traditional methods with much less labeled examples.