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Sign Language Gesture Recognition Using HMM

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Pattern Recognition and Image Analysis (IbPRIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10255))

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Abstract

Gesture recognition is very useful in everyday life for tasks related to computer-human interaction. Gesture recognition systems are usually tested with a very large, complete, standardised and intuitive database of gesture: sign language. Unfortunately, such data is typically very large and contains very similar data which makes difficult to create a low cost system that can differentiate a large enough number of signs. This makes difficult to create a useful tool for allowing deaf people to communicate with the hearing people. The present work presents a sign recognition system for the Spanish sign language. The experiments conducted include separated gesture recognition and sequences of gestures. This work extends previous work by augmenting the size of data set (91 signs), higher than most of the state of the art systems (around 20 gestures). Apart from that, the proposed recognition system performs recognition of dynamic gestures, in contrast to most studies that use static gestures, which are easier to recognise. Finally, the work studies the recognition of sequences of gestures corresponding to grammatically correct phrases in Spanish sign language. For both tasks, Hidden Markov Models are used as recognition models. Results presented for classification of separated gestures are compared with other usual classification techniques, showing a better recognition performance. The current data set, which has been captured using the Leap Motion sensor, will be publicly available to the research community in gesture recognition.

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Notes

  1. 1.

    https://www.leapmotion.com/product/desktop.

  2. 2.

    The data is available in https://github.com/Sasanita/spanish-sign-language-db.git.

  3. 3.

    The value of the movement is the weighted sum of several variables, such as the speed of each hand between two consecutive images [5].

  4. 4.

    http://www.fesord.org.

References

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Acknowledgements

Work partially supported by MINECO under grant DI-15-08169, by Sciling under its R+D programme, by MINECO/FEDER under project CoMUN-HaT (TIN2015-70924-C2-1-R), and by Generalitat Valenciana (GVA) under reference PROMETEOII/2014/030.

Author information

Authors and Affiliations

  1. Sciling S.L., Carrer del Riu 321, Pinedo, 46012, Valencia, Spain

    Zuzanna Parcheta

  2. Pattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain

    Carlos-D. Martínez-Hinarejos

Authors
  1. Zuzanna Parcheta
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  2. Carlos-D. Martínez-Hinarejos
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Corresponding author

Correspondence to Zuzanna Parcheta .

Editor information

Editors and Affiliations

  1. Universidade da Beira Interior , Covilhã, Portugal

    Luís A. Alexandre

  2. University Jaume I , Castellón, Spain

    José Salvador Sánchez

  3. University of the Algarve , Faro, Portugal

    João M. F. Rodrigues

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Parcheta, Z., Martínez-Hinarejos, CD. (2017). Sign Language Gesture Recognition Using HMM. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_46

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  • DOI: https://doi.org/10.1007/978-3-319-58838-4_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58837-7

  • Online ISBN: 978-3-319-58838-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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