References
- D. Jiang, Z. Zheng, G. Li, Y. Sun, J. Kong, G. Jiang, et al., "Gesture recognition based on binocular vision," Cluster Computing, vol. 22, no. 6, pp. 13261-13271, 2019. https://doi.org/10.1007/s10586-018-1844-5
- K. Vaesen, A. Visweswaran, S. Sinha, A. Bourdoux, B. van Liempd, and P. Wambacq, "Integrated 140 GHz FMCW radar for vital sign monitoring and gesture recognition," Microwave Journal, vol. 62, no. 6, pp. 50-58, 2019.
- X. A. Huang, Q. Wang, S. Zang, J. Wan, G. Yang, Y. Huang, and X. Ren, "Tracing the motion of finger joints for gesture recognition via sewing RGO-coated fibers onto a textile glove," IEEE Sensors Journal, vol. 19, no. 20, pp. 9504-9511, 2019. https://doi.org/10.1109/jsen.2019.2924797
- J. H. Sun, T. T. Ji, S. B. Zhang, J. K. Yang, and G. R. Ji, "Research on the hand gesture recognition based on deep learning," in Proceedings of 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Hangzhou, China, 2018, pp. 1-4.
- D. Zhu, R. Wei, W. Zhan, and Z. Hao, "Individual soldier gesture intelligent recognition system," in Proceedings of 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China, 2019, pp. 231-235.
- A. Ananthakumar, "Efficient face and gesture recognition for time sensitive application," in Proceedings of 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Las Vegas, NV, 2018, pp. 117-120.
- S. Sharma and S. Jain, "A static hand gesture and face recognition system for blind people," in Proceedings of 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2019, pp. 534-539.
- J. P. Sahoo, S. Ari, and S. K. Patra, "Hand gesture recognition using PCA based deep CNN reduced features and SVM classifier," in Proceedings of 2019 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS), Rourkela, India, 2019, pp. 221-224.
- T. Du, X. Ren, and H. Li, "Gesture recognition method based on deep learning," in Proceedings of 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Nanjing, China, 2018, pp. 782-787.
- I. Dhall, S. Vashisth, and G. Aggarwal, "Automated hand gesture recognition using a deep convolutional neural network model," in Proceedings of 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2020, pp. 811-816.
- T. H. Nascimento, F. A. A. Soares, H. A. Nascimento, M. A. Vieira, T. P. Carvalho, and W. F. de Miranda, "Netflix control method using smartwatches and continuous gesture recognition," in Proceedings of 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, Canada, 2019, pp. 1-4.
- X. Zhang and X. Wu, "Robotic control of dynamic and static gesture recognition," in Proceedings of 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), Shanghai, China, 2019, pp. 474-478.
- A. Kanawade, S. Varvadekar, D. R. Kalbande, and P. Desai, "Gesture and voice recognition in story telling application," in Proceedings of 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai, India, 2018, pp. 1-5.
- K. M. Kim and J. I. Choi, "Passengers' gesture recognition model in self-driving vehicles: gesture recognition model of the passengers' obstruction of the vision of the driver," in Proceedings of 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 239-242.
- S. A. Hoque, M. S. Haq, and M. Hasanuzzaman, "Computer vision based gesture recognition for desktop object manipulation," in Proceedings of 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 2018, pp. 1-6.
- J. Xue, Y. Zong, and Z. Yang, "Gesture recognition based on improved YCBCR space and multi-feature integration," Computer Applications and Software, vol. 33, no. 1, pp. 151-155, 2016.
- L. Lu, J. Zhang, Y. Zhu, and H. Liu, "A static gesture recognition method based on data glove," Journal of Computer-Aided Design & Computer Graphics, vol. 27, no. 12, pp. 2411-2416, 2015.
- Z. Cai, S. Wu, and J. Song, "Study on Hand Gesture Recognition and Portfolio Optimization Model Based on SVM," Journal of System Simulation, vol. 28, no. 8, pp. 1812-1817, 2016.
- K. H. Park and S. W. Lee, "Movement intention decoding based on deep learning for multiuser myoelectric interfaces," in Proceedings of 2016 4th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, South Korea, 2016, pp. 1-2.
- M. Atzori, M. Cognolato, and H. Muller, "Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands," Frontiers in Neurorobotics, vol. 10, article no. 9, 2016. https://doi.org/10.3389/fnbot.2016.00009
- W. Geng, Y. Du, W. Jin, W. Wei, Y. Hu, and J. Li, "Gesture recognition by instantaneous surface EMG images," Scientific Reports, vol. 6, article no. 36571, 2016. https://doi.org/10.1038/srep36571
- W. Wei, Y. Wong, Y. Du, Y. Hu, M. Kankanhalli, and W. Geng, "A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface," Pattern Recognition Letters, vol. 119, pp. 131-138, 2019. https://doi.org/10.1016/j.patrec.2017.12.005
- W. Fang, Y. Ding, F. Zhang, and J. Sheng, "Gesture recognition based on CNN and DCGAN for calculation and text output," IEEE Access, vol. 7, pp. 28230-28237, 2019. https://doi.org/10.1109/ACCESS.2019.2901930
- M. Tan, J. Zhou, K. Xu, Z. Peng, and Z. Ma, "Static hand gesture recognition with electromagnetic scattered field via complex attention convolutional neural Network," IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 4, pp. 705-709, 2020. https://doi.org/10.1109/LAWP.2020.2977995
- A. A. Alani, G. Cosma, A. Taherkhani, and T. M. McGinnity, "Hand gesture recognition using an adapted convolutional neural network with data augmentation," in Proceedings of 2018 4th International Conference on Information Management (ICIM), Oxford, UK, 2018, pp. 5-12.
- Z. Zhu, J. Li, L. Zhuo, and J. Zhang, "Extreme weather recognition using a novel fine-tuning strategy and optimized GoogLeNet," in Proceedings of 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia 2017, pp. 1-7.
- T. Fang, "A novel computer-aided lung cancer detection method based on transfer learning from GoogLeNet and median intensity projections," in Proceedings of 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China, 2018, pp. 286-290.
- K. Zhang, M. Sun, T. X. Han, X. Yuan, L. Guo, and T. Liu, "Residual networks of residual networks: multilevel residual networks," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1303-1314, 2018. https://doi.org/10.1109/tcsvt.2017.2654543
- X. Liu, J. Chen, Y. Wu, Y. Cui, Z. Ding, and S. Yang, "An optimized residual network with block-soft clustering for road extraction from remote sensing imagery," in Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, 2019, pp. 2767-2772.
- S. Bulusu, Q. Li, and P. K. Varshney, "On convex stochastic variance reduced gradient for adversarial machine learning," in Proceedings of 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Ottawa, Canada, 2019, pp. 1-5.
- H. T. Elshoush and E. A. Dinar, "Using adaboost and stochastic gradient descent (SGD) algorithms with R and orange software for filtering e-mail spam," in Proceedings of 2019 11th Computer Science and Electronic Engineering (CEEC), Colchester, UK, 2019, pp. 41-46.