A Combined Method Based on SVM and Online Learning with HOG for Hand Shape Recognition

In this paper, we propose a combined method for hand shape recognition. It consists of support vector machines (SVMs) and an online learning algorithm based on the perceptron. We apply HOG features to each method. First, our method estimates a hand shape of an input image by using SVMs. Here the online learning method with the perceptron uses the input image as new training data if the data is effective for relearning in the recognition process. Next, we select the final hand shape from the outputs of the SVMs and perceptron by using the score of SVMs. The combined method deals with a problem about decrease of the accuracy in the case that users change. Applying the online perceptron jointly leads to improvement of the accuracy. We compare the combined method with a method using only SVMs. The experimental result shows the effectiveness of the proposed method.