Hand pose estimation system based on combined features for mobile devices

摘要

Today's mobile devices or smartphones have a revolutionary impact on how we communicate, especially after the advent of devices like smart watches and Google glasses that require new ways to interact with them. To optimise the use of mobile devices, special input and output peripherals have been designed over the years to facilitate communication with them. The well known peripherals are the multi-touch screens. Smartphones are too small to work freely using their input screens. To solve this problem, recent research has focused on contactless and natural gestural interfaces. In this context, we propose a hand gesture recognition system for mobile devices as a simple way of communication with smartphones. In this work, we describe a hand gesture recognition system for Android devices based on a combination of HOG and LBP features and the SVM classifier. An accuracy rate of about 95% is obtained on the improved 'NUS database I'. In addition, we conduct experiments on different Android devices to know the impact of using such a recognition task on embedded systems like smartphones.