Research Area: Machine Learning
Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.
Keywords:
Hand Gesture Recognition
Single-Shot Multibox Detector
Deep Learning
Author(s) Name: Peng Liu,Xiangxiang Li,Haiting Cui,Shanshan Li,and Yafei Yuan
Journal name: Mobile Information Systems
Conferrence name:
Publisher name: Hindawi
DOI: 10.1155/2019/3410348
Volume Information:
