Hand pose estimation system based on combined features for mobile devices

作     者：Lahiani, Houssem Neji, Mahmoud 

作者机构：National School of Electronics and Telecommunications University of Sfax Tunisia Faculty of Economics and Management University of Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory Technopark of Sfax Tunis Road 10 km P.O. Box 242 SFAX 3021 Tunisia 

出 版 物：《International Journal of Intelligent Information and Database Systems》 (Int. J. Intell. Inf. Database Syst.)

年 卷 期：2020年第13卷第2-4期

页      面：436-453页

核心收录：

学科分类：0810[工学-信息与通信工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Gesture recognition 

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. © 2020 Inderscience Enterprises Ltd.