Name
Abstract | ||
|---|---|---|
Gesture rotation and zooming have significant impact on the gesture recognition system and can greatly reduce the recognition rate. In this paper, we propose a novel recognition method for the multi-scale multi-angle gestures in skin-like noise backgrounds based on HOG-LBP feature extraction. The proposed gesture recognition system consists of pretreatment, feature extraction and classification. First, the single Gaussian model (SGM) and K-means algorithm was used to extract gesture images from a skin-like noise background region. Then, a HOG-LBP feature descriptor is proposed to represent multi-scale multi-angle gesture information. The HOG component provides the gesture edge gradient information and the LBP component provides the texture feature information, which can compensate for the lack of rotation invariance of a single feature and improve the recognition rate of gestures at multiple scales and multiple angles. Finally, the SVM classifier is utilized to realize the gesture classification. Experiment results on the home-made data sets show that the proposed method can achieve 99.01% recognition rate. Experiments on the NUS database and the MUGD database also demonstrate the performance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICARCV.2018.8581098 | 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) |
Keywords | Field | DocType |
K-means algorithm,gesture rotation,SGM,single Gaussian model,SVM classifier,gesture classification,texture feature information,gesture edge gradient information,multiscale multiangle gesture information,HOG-LBP feature descriptor,skin-like noise background region,gesture images,HOG-LBP feature extraction,gesture recognition system,zooming,multiscale multiangle gestures | Data set,Feature descriptor,Pattern recognition,Invariant (physics),Computer science,Gesture,Gesture recognition,Zoom,Feature extraction,Control engineering,Artificial intelligence,Gaussian network model | Conference |
ISSN | ISBN | Citations |
2474-2953 | 978-1-5386-9583-8 | 0 |
PageRank | References | Authors |
0.34 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Zhou | 1 | 0 | 0.34 |
| Yanhong Liu | 2 | 30 | 10.15 |
| Keqiang Li | 3 | 583 | 52.39 |