Synthetic Training of Deep CNN for 3D Hand Gesture Identification

Chun Jen Tsai, Yun Wei Tsai, Song Ling Hsu, Ya Chiu Wu

研究成果: Conference contribution同行評審

12 引文 斯高帕斯(Scopus)

摘要

In this paper, we present some experiments and investigations on a synthetically-trained neural network for the 3D hand gesture identification problem. The training process of a deep-learning neural network typically requires a large amount of training data to converge to a valid recognition model. However, in practice, it is difficult to obtain a large set of tagged real-data for the training purposes. In this paper, we investigate the plausibility of combining a large set of computer-generated 3D hand images with few real-camera images to form the training data set for the 3D hand gesture recognition applications. It is shown that by adding 0.09% of real images to the synthetic training data set, the recognition accuracy are raised from 37.5% to 77.08% for the problem of identifying 24 classes of hand gestures of an unknown user whose hand was not used in the training data set. In this paper, we have shown that the effect of the few real images to the trained CNN models mainly falls upon the fully-connected layers.

原文English
主出版物標題Proceedings - 2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面165-170
頁數6
ISBN(電子)9781509065363
DOIs
出版狀態Published - 1 7月 2017
事件2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017 - Prague, Czech Republic
持續時間: 20 5月 201722 5月 2017

出版系列

名字Proceedings - 2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017
2018-January

Conference

Conference2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017
國家/地區Czech Republic
城市Prague
期間20/05/1722/05/17

指紋

深入研究「Synthetic Training of Deep CNN for 3D Hand Gesture Identification」主題。共同形成了獨特的指紋。

引用此