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Recognition of a real-time signer-independent static Farsi sign language based on fourier coefficients amplitude

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Abstract

A sign language is a language which uses manual communication and body language to convey meaning, as opposed to acoustically conveyed sound patterns. This can involve simultaneously combining hand shapes, orientation and movement of the hands, arms or body, and facial expressions to fluidly express a speaker’s thoughts. Sign language is a preliminary communication way for individuals with hearing and speech problems. Considering that more than a hundred million people all around the world are annoyed by hearing loss and impaired speech, it is needed to design a system for automatic sign language interpreter as an interface between deaf-and-dumb and ordinary people can feel it strongly. Given this, in this article we aimed to design such a computer vision-based translation interface. Farsi sign language recognition is one of the most challenging fields of study is given because of some features such as the wide range of similar gestures, hands orientation, complicated background, and ambient light variation. A Farsi sign language recognition system is based on computer vision which is capable of real-time gesture processing and is independent of the signer. Furthermore, there is no need to use glove or marker by the signer in the proposed system. After hand segmentation from video frames, the proposed algorithm extracts boundary of the dominant hand to compute the normalized accumulation angle and represents the boundary, so that the invariance to transition and scale change of the features is realized at this stage. Afterward, Fourier coefficients amplitude is extracted as preferred features at the frequency domain, while invariance to rotation of the features is added at this point. Then the frequency features, as extracted features for gesture recognition, are applied to inputs of feed-forward multilayer perception neural network. The proposed method is presented to make recognition system independent of the signer and retrofit it against signer’s distance changes from camera using features of powerful invariant extraction against transition, scale change, and rotation. Data classification is carried out by three classifiers including Bayes, K-NN, and neural network. Performance of the classifiers was also compared. Training set of gestures comprised 250 samples for 10 gestures and 5 positions and orientations that were performed by 5 individuals. Recognition results showed an outstanding recognition rate of the system.

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References

  1. Murakami K, Taguchi H (1991) “Gesture recognition using recurrent neural networks”. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp 237–242

  2. Liang R-H, Ouhyoung M (1998) “A real-time continuous gesture recognition system for sign language”. In: Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pp 558–567

  3. Gaus YFA, Wong F (2012) “Hidden Markov Model-based gesture recognition with overlapping hand-head/hand-hand estimated using kalman filter”. In: Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference on, pp 262–267

  4. Shen X, Hua G, Williams L, Wu Y (2012) Dynamic hand gesture recognition: an exemplar-based approach from motion divergence fields. Image Vis Comput 30:227–235

    Article  Google Scholar 

  5. Al-Jarrah O, Halawani A (2001) Recognition of gestures in Arabic sign language using neuro-fuzzy systems. Artif Intell 133:117–138

    Article  MATH  Google Scholar 

  6. Karami A, Zanj B, Sarkaleh AK (2011) Persian sign language (PSL) recognition using wavelet transform and neural networks. Expert Syst Appl 38:2661–2667

    Article  Google Scholar 

  7. Shangeetha R, Valliammai V, Padmavathi S (2012) “Computer vision based approach for Indian Sign Language character recognition”. In: Machine Vision and Image Processing (MVIP), 2012 International Conference on, vol no, pp 14–15

  8. Sharma R, Nemani Y, Kumar S, Kane L, Khanna P (2013) “Recognition of Single Handed Sign Language Gestures using Tracing Descriptor”. In: Proceedings of the World Congress on Engineering, Contour

  9. Altun O, Albayrak S (2011) Turkish fingerspelling recognition system using Generalized Hough Transform, interest regions, and local descriptors. Pattern Recognit Lett 32:1626–1632

    Article  Google Scholar 

  10. Futane PR, Dharaskar RV (2012) “Video gestures identification and recognition using Fourier descriptor and general fuzzy minmax neural network for subset of Indian sign language”. In: HIS, pp 525–530

  11. Saengsri S, Niennattrakul V, Ratanamahatana CA (2012) “TFRS: Thai finger-spelling sign language recognition system”. In: Digital Information and Communication Technology and it’s Applications (DICTAP), 2012 Second International Conference on, pp 457–462

  12. Kurdyumov R, Ho P, Ng J (2011) “Sign language classification using webcam images”

  13. Ravikiran J, Mahesh K, Mahishi S, Dheeraj R, Sudheender S, Pujari NV (2009) “Finger detection for sign language recognition”. In: Proceedings of the international MultiConference of Engineers and Computer Scientists, pp 18–20

  14. Nixon M (2008) Feature extraction and image processing. Academic Press, Cambridge

    Google Scholar 

  15. Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin-color modeling and detection methods. Pattern Recognit 40:1106–1122

    Article  MATH  Google Scholar 

  16. Nabiyev V, Günay A (2008) “Towards a biometric purpose image filter according to skin detection”. In: The Second International Conference “Problems of Cybernetics and Informatics” September, pp 10–12

  17. Prokop RJ, Reeves AP (1992) A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP Graph Models Image Process 54:438–460

    Article  Google Scholar 

  18. Goh P (2005) “Automatic recognition of Auslan finger-spelling using hidden Markov models”. Undergraduate thesis, University of Western Australia

  19. Csetverikov D (2001) Basic algorithms for digital image analysis: a course. Eötvös Loröand University, Budapest

    Google Scholar 

  20. Gonzales RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, New Jersey. ISBN 0-201-18075-8

    Google Scholar 

  21. Demuth H, Beale M (1993) “Neural network toolbox for use with MATLAB”

  22. Brashear Helene et al (2003) “Using multiple sensors for mobile sign language recognition”

  23. Yuan Quan et al (2002) “Recognition of strong and weak connection models in continuous sign language”. In: Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol 1, IEEE

  24. Parashar AS (2003) “Representation and interpretation of manual and non-manual information for automated American sign language recognition”

  25. Vogler C, Metaxas D (1999) “Parallel hidden markov models for american sign language recognition”. In: Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, vol 1, IEEE

  26. Šušić MZ et al (2012) “Recognition and classification of deaf signs using neural networks”. In: Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on, IEEE

  27. Kao Chang-Yi, Fahn Chin-Shyurng (2011) A human-machine interaction technique: hand gesture recognition based on hidden Markov models with trajectory of hand motion. Procedia Eng 15:3739–3743

    Article  Google Scholar 

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Acknowledgments

I would like to thank my two dear brothers Amirhossein and Roohella Zare, for all their encouragement and support during the development of this project, my beloved wife, for all the love and devotion she professes me every day, and finally to my friends and colleagues for their invaluable help at providing the data set. Without all of them this project would not have been possible.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

    Ali Asghar Zare & Seyed Hamid Zahiri

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  1. Ali Asghar Zare
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  2. Seyed Hamid Zahiri
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Correspondence to Ali Asghar Zare.

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Zare, A.A., Zahiri, S.H. Recognition of a real-time signer-independent static Farsi sign language based on fourier coefficients amplitude. Int. J. Mach. Learn. & Cyber. 9, 727–741 (2018). https://doi.org/10.1007/s13042-016-0602-3

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  • DOI: https://doi.org/10.1007/s13042-016-0602-3

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