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Sign language recognition using convolutional neural networks

Lionel Pigou (UGent) , Sander Dieleman (UGent) , Pieter-Jan Kindermans (UGent) and Benjamin Schrauwen (UGent)
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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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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://dx.doi.org/10.1007/978-3-319-16178-5_40}},
  year         = {{2015}},
}

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