%0 Journal Article %1 zhang2018handsense %A Zhang, Zhenyuan %A Tian, Zengshan %A Zhou, Mu %D 2018 %I Springer %J Journal of Ambient Intelligence and Humanized Computing %K feature fine-grained-gesture gesture hand handsense real recognition spatiotemporal %P 1-16 %R 10.1007/s12652-018-0989-7 %T HandSense: smart multimodal hand gesture recognition based on deep neural networks %U https://doi.org/10.1007/s12652-018-0989-7 %X Hand gesture recognition (HGR) is a promising enabler for human--computer interaction (HCI). Hand gestures are normally classified into multi-modal actions, including static gestures, fine-grained dynamic gestures, and coarse-grained dynamic gestures. Among them, the fine-grained action detection is limited under the small-scale image region condition. To solve this problem, we propose the HandSense, a new system for the multi-modal HGR based on a combined RGB and depth cameras to improve the fine-grained action descriptors as well as preserve the ability to perform general action recognition. First of all, two interconnected 3D convolutional neural network (3D-CNN) are employed to extract the spatial--temporal features from the RGB and depth images. Second, these spatial--temporal features are integrated into a fusion feature. Finally, the Support Vector Machine (SVM) is used to recognize different gestures based on the fusion feature. To validate the effectiveness of the HandSense, the extensive experiments are conducted on the public gesture dataset, namely the SKIG hand gesture dataset. In addition, the feasibility of the proposed system is also demonstrated by using a challenging multi-modal RGB-Depth hand gesture dataset.

@article{zhang2018handsense, abstract = {Hand gesture recognition (HGR) is a promising enabler for human--computer interaction (HCI). Hand gestures are normally classified into multi-modal actions, including static gestures, fine-grained dynamic gestures, and coarse-grained dynamic gestures. Among them, the fine-grained action detection is limited under the small-scale image region condition. To solve this problem, we propose the HandSense, a new system for the multi-modal HGR based on a combined RGB and depth cameras to improve the fine-grained action descriptors as well as preserve the ability to perform general action recognition. First of all, two interconnected 3D convolutional neural network (3D-CNN) are employed to extract the spatial--temporal features from the RGB and depth images. Second, these spatial--temporal features are integrated into a fusion feature. Finally, the Support Vector Machine (SVM) is used to recognize different gestures based on the fusion feature. To validate the effectiveness of the HandSense, the extensive experiments are conducted on the public gesture dataset, namely the SKIG hand gesture dataset. In addition, the feasibility of the proposed system is also demonstrated by using a challenging multi-modal RGB-Depth hand gesture dataset.}, added-at = {2019-11-15T23:07:01.000+0100}, author = {Zhang, Zhenyuan and Tian, Zengshan and Zhou, Mu}, biburl = {https://www.bibsonomy.org/bibtex/2a9e8b9b2a5425668612d5f0cab61f84e/jpmor}, day = 23, description = {HandSense: smart multimodal hand gesture recognition based on deep neural networks | SpringerLink}, doi = {10.1007/s12652-018-0989-7}, interhash = {98e0f756a34345da7f40e5b9da6577ae}, intrahash = {a9e8b9b2a5425668612d5f0cab61f84e}, issn = {1868-5145}, journal = {Journal of Ambient Intelligence and Humanized Computing}, keywords = {feature fine-grained-gesture gesture hand handsense real recognition spatiotemporal}, language = {English}, month = {08}, pages = {1-16}, publisher = {Springer}, school = {Chongqing University of Posts and Telecommunications (CQUPT)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {HandSense: smart multimodal hand gesture recognition based on deep neural networks}, url = {https://doi.org/10.1007/s12652-018-0989-7}, year = 2018 }