Abstract
For an effective vision-based HCI system, inference from natural means of sources (i.e. hand) is a crucial challenge in unconstrained environment. In this paper, we have aimed to build an interaction system through hand posture recognition for static finger spelling American Sign Language (ASL) alphabets and numbers. Unlike the interaction system based on speech, the coarticulation due to hand shape, position and movement influences the different aspects of sign language recognition. Due to this, we have computed the features which are invariant to translation, rotation and scaling. Considering these aspects as the main objectives of this research, we have proposed a three-step approach: first, features vector are computed using two moment based approaches namely Hu-Moment along with geometrical features and Zernike moment. Second, the categorization of symbols according to the fingertip is performed to avoid mis-classification among the symbols. Third, the extracted set of two features vectors (i.e. Hu-Moment with geometrical features and Zernike moment) are trained by Support Vector Machines (SVM) for the classification of the symbols. Experimental results of the proposed approaches achieve recognition rate of 98.5% using Hu-Moment with geometrical features and 96.2% recognition rate using Zernike moment for ASL alphabets and numbers demonstrating the dominating performance of Hu-Moment with geometrical features over Zernike moments.
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References
Jaimes, A., Sebe, N.: Multimodal human-computer interaction: A survey. Computer Vision and Image Understanding, 116–134 (2007)
Karray, F., Alemzadeh, M., Saleh, J., Arab, M.: Human-computer interaction: Overview on state of the art. International Journal on Smart Sensing and Intelligent Systems 1, 137–159 (2008)
Bowden, R., Zisserman, A., Kadir, T., Brady, M.: Vision based interpretation of natural sign languages. In: Proc. 3rd ICCVS, pp. 391–401 (2003)
Hussain, M.: Automatic recognition of sign language gestures. Master thesis, Jordan University of Science and Technology (1999)
Handouyahia, M., Ziou, D., Wang, S.: Sign language recognition using moment-based size functions. In: Int. Conf. of Vision Interface, pp. 210–216 (1999)
Malassiotis, S., Strintzis, M.: Real-time hand posture recognition using range data. Image and Vision Computing 26, 1027–1037 (2008)
Licsar, A., Sziranyi, T.: Supervised training based hand gesture recognition system. In: ICPR, pp. 999–1002 (2002)
Freeman, W., Roth, M.: Orientation histograms for hand gesture recognition. In: Int. Workshop on Automatic FG Recognition, pp. 296–301 (1994)
Hu, M.: Visual pattern recognition by moment invariants. IRE Trans. on Information Theory 8, 179–187 (1962)
Maitra, S.: Moment invariants. In: IEEE Conf. on CVPR., vol. 67, pp. 697–699 (1979)
Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Journal of Pattern Recognition 26, 164–174 (1993)
Davis, J., Bradski, G.: Real-time motion template gradients using intel cvlib. In: IEEE ICCV Workshop on Framerate Vision (1999)
Teague, M.: Image analysis via the general theory of moments. Journal of the Optical Society of America, 920–930 (1980)
Bailey, R., Srinath, M.: Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Trans. on PAMI 18, 389–399 (1996)
Kim, D.H., Kim, M.J.: A curvature estimation for pen input segmentation in sketch-based modeling. Computer-Aided Design 38, 238–248 (2006)
Cristianini, N., Taylor, J.: An Introduction to Support Vector Machines and other kernel based learning methods. Cambridge University Press, Cambridge (2001)
Lin, C., Weng, R.: Simple probabilistic predictions for support vector regression. Technical report, National Taiwan University (2004)
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Rashid, O., Al-Hamadi, A., Michaelis, B. (2010). Utilizing Invariant Descriptors for Finger Spelling American Sign Language Using SVM. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_25
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DOI: https://doi.org/10.1007/978-3-642-17289-2_25
Publisher Name: Springer, Berlin, Heidelberg
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