American Sign Language Alphabet Recognition Using Microsoft Kinect
Abstract
American Sign Language (ASL) alphabet recognition using marker-less vision sensors is a challenging task due to the complexity of ASL alphabet signs, self-occlusion of the hand, and limited resolution of the sensors. This paper describes a new method for ASL alphabet recognition using a low-cost depth camera, which is Microsoft's Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-seeking method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Forest (RF) classifier is built to recognize ASL signs using the joint angles. To validate the performance of this method, we used a publicly available dataset from Surrey University. The results have shown that our method can achieve above 90% accuracy in recognizing 24 static ASL alphabet signs, which is significantly higher in comparison to the previous benchmarks.
Cite
Text
Dong et al. "American Sign Language Alphabet Recognition Using Microsoft Kinect." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301347Markdown
[Dong et al. "American Sign Language Alphabet Recognition Using Microsoft Kinect." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/dong2015cvprw-american/) doi:10.1109/CVPRW.2015.7301347BibTeX
@inproceedings{dong2015cvprw-american,
title = {{American Sign Language Alphabet Recognition Using Microsoft Kinect}},
author = {Dong, Cao and Leu, Ming C. and Yin, Zhaozheng},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {44-52},
doi = {10.1109/CVPRW.2015.7301347},
url = {https://mlanthology.org/cvprw/2015/dong2015cvprw-american/}
}