A Robust Hand Gesture Recognition Method via Convolutional Neural Network

Abstract

Hand gesture plays an important role in nonverbal communication and natural human-computer interaction. However, the complex hand gesture structure and various environment factors lead to low recognition rate. For instance, hand gesture depends on individuals, and different individuals' hands are with different sizes and postures, in addition, unconstrained environmental illumination also influences hand gesture recognition performance. Therefore, hand gesture recognition is still a challenging issue. This paper proposes a robust method for hand gesture recognition based on convolutional neural network, which is utilized to automatically extract the spatial and semantic feature of hand gesture. Our method consists of a modified Convolutional Neural Network structure and data preprocessing, which corporately increase hand gesture recognition performance. The experimental results on both Cambridge Hand Gesture Dataset and self-constructed dataset show that the proposed method is effective and competitive.