Open AccessMonocular Multi-feature Fusion Hand Gesture Recognition Method Based on Multi-core Learning
Author(s) -
Kai Zhuang
Publication year - 2019
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 - 10.1088/1742-6596/1288/1/012054
Subject(s) - artificial intelligence , computer science , gesture recognition , gesture , computer vision , segmentation , monocular , feature extraction , monocular vision , generalization , feature (linguistics) , pattern recognition (psychology) , core (optical fiber) , mathematics , telecommunications , linguistics , mathematical analysis , philosophy
Based on multi-core learning, this paper uses the method of skin color segmentation, fingertip distance detection and extraction and gesture contour extraction, which realizes the fusion of local features and overall features and makes the accuracy of hand gesture classification, reach 92%. Compared with traditional gesture recognition based on binocular camera, our algorithm using multi-core learning has the same accuracy as the recognition based on binocular camera, and the complexity and cost of our algorithm are lower. Thus, our algorithm has broader application prospects. Compared with gesture recognition based on monocular camera, our algorithm is more suitable for the background environment of gesture classification. In other words, it has better generalization ability.