Sign language recognition using convolutional neural networks
- Author
- Lionel Pigou (UGent) , Sander Dieleman (UGent) , Pieter-Jan Kindermans (UGent) and Benjamin Schrauwen (UGent)
- Organization
- Abstract
- There is an undeniable communication problem between the Deaf community and the hearing majority. Innovations in automatic sign language recognition try to tear down this communication barrier. Our contribution considers a recognition system using the Microsoft Kinect, convolutional neural networks (CNNs) and GPU acceleration. Instead of constructing complex handcrafted features, CNNs are able to auto- mate the process of feature construction. We are able to recognize 20 Italian gestures with high accuracy. The predictive model is able to gen- eralize on users and surroundings not occurring during training with a cross-validation accuracy of 91.7%. Our model achieves a mean Jaccard Index of 0.789 in the ChaLearn 2014 Looking at People gesture spotting competition.
- Keywords
- convolutional neural network, deep learning, sign language recognition, gesture recognition
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-5796137
- MLA
- Pigou, Lionel, et al. “Sign Language Recognition Using Convolutional Neural Networks.” Lecture Notes in Computer Science, Springer, 2015, pp. 572–78, doi:10.1007/978-3-319-16178-5_40.
- APA
- Pigou, L., Dieleman, S., Kindermans, P.-J., & Schrauwen, B. (2015). Sign language recognition using convolutional neural networks. Lecture Notes in Computer Science, 572–578. https://doi.org/10.1007/978-3-319-16178-5_40
- Chicago author-date
- Pigou, Lionel, Sander Dieleman, Pieter-Jan Kindermans, and Benjamin Schrauwen. 2015. “Sign Language Recognition Using Convolutional Neural Networks.” In Lecture Notes in Computer Science, 572–78. Springer. https://doi.org/10.1007/978-3-319-16178-5_40.
- Chicago author-date (all authors)
- Pigou, Lionel, Sander Dieleman, Pieter-Jan Kindermans, and Benjamin Schrauwen. 2015. “Sign Language Recognition Using Convolutional Neural Networks.” In Lecture Notes in Computer Science, 572–578. Springer. doi:10.1007/978-3-319-16178-5_40.
- Vancouver
- 1.Pigou L, Dieleman S, Kindermans P-J, Schrauwen B. Sign language recognition using convolutional neural networks. In: Lecture Notes in Computer Science. Springer; 2015. p. 572–8.
- IEEE
- [1]L. Pigou, S. Dieleman, P.-J. Kindermans, and B. Schrauwen, “Sign language recognition using convolutional neural networks,” in Lecture Notes in Computer Science, Zürich, Switserland, 2015, pp. 572–578.
@inproceedings{5796137,
abstract = {{There is an undeniable communication problem between the Deaf community and the hearing majority. Innovations in automatic sign language recognition try to tear down this communication barrier. Our contribution considers a recognition system using the Microsoft Kinect, convolutional neural networks (CNNs) and GPU acceleration. Instead of constructing complex handcrafted features, CNNs are able to auto- mate the process of feature construction. We are able to recognize 20 Italian gestures with high accuracy. The predictive model is able to gen- eralize on users and surroundings not occurring during training with a cross-validation accuracy of 91.7%. Our model achieves a mean Jaccard Index of 0.789 in the ChaLearn 2014 Looking at People gesture spotting competition.}},
author = {{Pigou, Lionel and Dieleman, Sander and Kindermans, Pieter-Jan and Schrauwen, Benjamin}},
booktitle = {{Lecture Notes in Computer Science}},
isbn = {{978-3-319-16178-5;}},
issn = {{0302-9743}},
keywords = {{convolutional neural network,deep learning,sign language recognition,gesture recognition}},
language = {{eng}},
location = {{Zürich, Switserland}},
pages = {{572--578}},
publisher = {{Springer}},
title = {{Sign language recognition using convolutional neural networks}},
url = {{http://doi.org/10.1007/978-3-319-16178-5_40}},
year = {{2015}},
}
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