Discriminating features learning in hand gesture classification
The advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More spe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2014.0426 |
| _version_ | 1797684540088516608 |
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| author | Feng Jiang Cuihua Wang Yang Gao Shen Wu Debin Zhao |
| author_facet | Feng Jiang Cuihua Wang Yang Gao Shen Wu Debin Zhao |
| author_sort | Feng Jiang |
| collection | DOAJ |
| description | The advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More specifically, histogram of oriented gradient feature, local binary pattern feature, structure feature and three‐dimensional voxel feature are first extracted from RGB images and depth images, then these features are further reduced with a novel deflation orthogonal discriminant analysis, which enhances the discriminative ability of the features with supervised subspace projection. The extensive experimental results show that the proposed method improves the HGR performance significantly. |
| first_indexed | 2024-03-12T00:31:11Z |
| format | Article |
| id | doaj.art-65a4ab47ed304afc8fd87b7b6e8e6804 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:31:11Z |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-65a4ab47ed304afc8fd87b7b6e8e68042023-09-15T10:21:07ZengWileyIET Computer Vision1751-96321751-96402015-10-019567368010.1049/iet-cvi.2014.0426Discriminating features learning in hand gesture classificationFeng Jiang0Cuihua Wang1Yang Gao2Shen Wu3Debin Zhao4School of ComputerHarbin Institute of TechnologyHarbinPeople's Republic of ChinaSchool of ComputerHarbin Institute of TechnologyWeihaiPeople's Republic of ChinaSchool of ComputerHarbin Institute of TechnologyHarbinPeople's Republic of ChinaSchool of ComputerHarbin Institute of TechnologyHarbinPeople's Republic of ChinaSchool of ComputerHarbin Institute of TechnologyHarbinPeople's Republic of ChinaThe advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More specifically, histogram of oriented gradient feature, local binary pattern feature, structure feature and three‐dimensional voxel feature are first extracted from RGB images and depth images, then these features are further reduced with a novel deflation orthogonal discriminant analysis, which enhances the discriminative ability of the features with supervised subspace projection. The extensive experimental results show that the proposed method improves the HGR performance significantly.https://doi.org/10.1049/iet-cvi.2014.0426hand gesture classificationfeatures learning discriminationKinectHGR researchfeatures extractionred, green and blue images |
| spellingShingle | Feng Jiang Cuihua Wang Yang Gao Shen Wu Debin Zhao Discriminating features learning in hand gesture classification IET Computer Vision hand gesture classification features learning discrimination Kinect HGR research features extraction red, green and blue images |
| title | Discriminating features learning in hand gesture classification |
| title_full | Discriminating features learning in hand gesture classification |
| title_fullStr | Discriminating features learning in hand gesture classification |
| title_full_unstemmed | Discriminating features learning in hand gesture classification |
| title_short | Discriminating features learning in hand gesture classification |
| title_sort | discriminating features learning in hand gesture classification |
| topic | hand gesture classification features learning discrimination Kinect HGR research features extraction red, green and blue images |
| url | https://doi.org/10.1049/iet-cvi.2014.0426 |
| work_keys_str_mv | AT fengjiang discriminatingfeatureslearninginhandgestureclassification AT cuihuawang discriminatingfeatureslearninginhandgestureclassification AT yanggao discriminatingfeatureslearninginhandgestureclassification AT shenwu discriminatingfeatureslearninginhandgestureclassification AT debinzhao discriminatingfeatureslearninginhandgestureclassification |