The Realization and Optimization of Static Gesture Recognition System Based on Representation Learning

Abstract

Gesture, which carries a large amount of human meanings is one of the most important and complicated way in human-computer interaction. Representation learning, the representation of complex objects and their eigenvalue can be discovered by machine learning. It can have an objective and comprehensive representation of experimental sample. This paper applies representation learning to the feature extraction of gestures and establishes a static gesture recognition system. The system uses Leap Motion as a teaching data collector. As a result, the process of gesture segmentation and the error from it would be avoided. In order to reduce model training and recognition time, this system uses support vector machine algorithm, an algorithm with fast response speed and high recognition rate, as a classifier. In the process of feature extraction, the machine learning algorithm itself is used to explore the representation of gestures and the extraction of feature. Therefore, the traditional empirical method of extraction would be replaced and the precision and stability of gesture recognition system would be improved. Experimental results show that compared with the traditional gesture recognition system, the improved gesture recognition system has higher recognition rate, accuracy and anti-interference ability.