Deep Feature Extraction and Multi-feature Fusion for Similar Hand Gesture Recognition

Abstract

Gesture recognition plays an important role in human computer interaction, but the accuracy is unsatisfactory in complex gestures with slight discrimination. In this paper, a framework facing to recognize complex and similar gestures is presented. In the framework, a parallel connection structure of convolutional neural network (CNN) is designed to extract deep features of complex and similar gestures from RGBD images. Then, a novel feature fusion method is proposed to achieve multi-feature fusion and dimension reduction simultaneously. According to experimental results on American Sign Language (ASL) dataset, the proposed framework reaches 99.042% recognition rate and outperforms current state-of-the-art methods.