Sparse principle motion component for one-shot gesture recognition

Abstract

With the rapid development of computer vision technology, gesture recognition has attracted much attention in recent years. However, the traditional gesture recognition methods waste a lot of time in the process of building a model with a large number of examples. To tackle the above problems, in this paper we propose sparse PCA based principle motion component (SPMC) method for one-shot gesture recognition, which can properly enhance recognition accuracy only with few training examples and unspecialized sensors. To evaluate the SPMC method, we conduct one-shot gesture recognition experiments on ChaLearn Gesture Dataset. Experimental results show that the proposed approach can improve the accuracy of gesture recognition.