Open AccessMouse Cursor Control Using Hand Gesture Recognition Based on PHOG-Improved LBP and K-NN Classification
Author(s) -
Yi Jin,
Kang Chen,
Jun Wang,
Mingmin Ou,
Wu Yi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012048
Subject(s) - gesture , computer science , artificial intelligence , computer vision , gesture recognition , histogram , local binary patterns , histogram of oriented gradients , pattern recognition (psychology) , cursor (databases) , image (mathematics)
A real-time mouse cursor control system using static gestures was designed to achieve the goal of human-computer interaction (HCI) in this paper. The system uses the computer’s own camera or external USB camera to collect video data, detect and recognize gestures of people in the video, and control the cursor movement or click in real time based on the gesture. To improve the recognition accuracy of hand gesture images detection, a method of hand gesture images detection based on PHOG + Improved LBP + K-NN was proposed in this paper. In order to improve the real-time performance of the system, the system determines whether the current frame has human hands by skin color detection. When human hands are detected, Pyramid Histogram of Oriented Gradients (PHOG) features and the improved Local Binary Pattern (LBP) features are further extracted. After fusing PHOG features and improved LBP features, k-nearest neighbor classification (K-NN) is used to implement gesture recognition. Six different gestures were tested 50 times with different angles, different lights and no skin tone in the background. The experimental results show that the system has good recognition performance.