| Title: | OS13-4 Recognition of Finger Spelling from Color Images Using Deep Learning |
|---|---|
| Publication: | ICAROB2018 |
| Volume: | 23 |
| Pages: | 542-545 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2018.OS13-4 |
| Author(s): | Yusuke Yamaguchi, Masayoshi Tabuse |
| Publication Date: | February 2, 2018 |
| Keywords: | Recognition of Finger Spelling, Deep Learning, Faster R-CNN, Color Image |
| Abstract: | We have developed a system to recognize finger spelling in Japanese sign language using deep neural networks. As deep neural networks, we adopt Faster R-CNN. By defining an output class for each finger letter and learning the object detection network, it is possible to output where the finger letter exists in the input image. This method does not require depth cameras, magnetic sensors, or other special equipment when used. Furthermore, this does not require preprocessing that extracts the hand region using human skin colored regions and color gloves used in other methods using color images. We synthesized a training data set by processing images taken with Kinect. As the test data, we input images of performing finger letters into the trained network and check the score of the output area and class. |
| PDF File: | https://alife-robotics.co.jp/members2018/icarob/data/html/data/OS_pdf/OS13/OS13-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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