標題: | Synthetic Training of Deep CNN for 3D Hand Gesture Identification |
作者: | Tsai, Chun-Jen Tsai, Yun-Wei Hsu, Song-Ling Wu, Ya-Chiu 資訊工程學系 Department of Computer Science |
關鍵字: | Deep-learning neural networks;convolutional neural networks;hand gesture identification;3D hand models |
公開日期: | 1-Jan-2017 |
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. |
URI: | http://dx.doi.org/10.1109/ICCAIRO.2017.40 http://hdl.handle.net/11536/147170 |
DOI: | 10.1109/ICCAIRO.2017.40 |
期刊: | 2017 INTERNATIONAL CONFERENCE ON CONTROL, ARTIFICIAL INTELLIGENCE, ROBOTICS & OPTIMIZATION (ICCAIRO) |
起始頁: | 165 |
結束頁: | 170 |
Appears in Collections: | Conferences Paper |