A committee machine implementing the pattern recognition module for fingerspelling applications

ABSTRACT

In several countries, deaf communities adopt the sign language as their official and natural language. This fact inserts a new field for the software application development that can improve sign language dissemination and social inclusion of deaf people. Alphabetic character-based applications, like games and educational softwares, can be adapted to run as fingerspelling-based applications, in which the inputs are signs (static images or videos) rather than letters (typed letters). In this paper, we present a Pattern Recognition Module, implemented by Committee Machine, for fingerspelling applications. The committee experts are built with supervised and unsupervised Fuzzy Learning Vector Quantization models using the "boosting by filtering" strategy. The module was tested in a specific sign language context considering hand configurations and hand movements.

References

1. J. C. Bezdek, E. C. Tsao, and N. R. Pal. Fuzzy kohonen clustering networks. In IEEE International Conference on Fuzzy Systems, pages 1035--1043, 1992.
2. F.-S. Chen, C.-M. Fu, and C.-L. Huang. Hand gesture recognition using a real-time tracking method and hidden markov models. Image and Vision Computing, 21(8):745--758, August 2003.
3. F. Chung and T. Lee. Fuzzy learning vector quantization. In IEEE International Joint Conference on Neural Networks, pages 2793--2742, 1993.
4. D. B. Dias, R. C. B. Madeo, T. Rocha, H. H. Bíscaro, and S. M. Peres. Hand movement recognition for brazilian sign language: A study using distance-based neural networks. In International Joint Conference on Neural Networks, pages 697--704. IEEE, 2009.
5. L. Fausett. Fundamentals of Neural Networks: architectures, algorithms, and appliations. Prentice-Hall, Inc., New Jersey, USA, 1994.
6. R. C. Gonzalez and R. Woods. Image Processing using MATLAB. Prentice Hall Inc, New Jersey, 2002.
7. S. Haykin. Neural Networks: a comprehensive foundation. Prentice Hall Inc, NJ, 2nd edition, 1998.
8. M. Lamar, M. S. Bhuiyan, and A. Iwata. Biologically Motivated Computer Vision - LNCS, chapter T-CombNET - A Neural Network Dedicated to Hand Gesture Recognition, pages 613--622. Springer, 2000.
9. S. Mitra and T. Acharya. Gesture recognition: A survey. IEEE Trans. on Systems, Man, and Cybernetics - Part C, 37(3), May 2007.
10. V. I. Pavlovic, R. Sharma, and T. S. Huang. Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:677--695, 1997.
11. A. Ramamoorthy, N. Vaswani, S. Chaudhury, and S. Banerjee. Recognition of dynamic hand gestures. Pattern Recognition, 36:2069--2081, 2003.
12. T. Starner, J. Weaver, and A. Pentland. Real-time american sign language recognition from video using hidden markov models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20:1371--1375, 1998.
13. M.-H. Yang and N. Ahuja. Recognizing hand gesture using motion trajectories. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 466--472, Juny 1999.
14. H. S. Yoon, J. Soh, Y. J. Bae, and H. S. Yang. Hand gesture recognition using combined features of location, angle and velocity. Pattern Recognition, 34:1491--1501, 2001.

Index Terms

• Computer systems organization

  • Architectures

    • Other architectures

      • Special purpose systems

• Computing methodologies

  • Artificial intelligence

    • Computer vision

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning approaches

      • Classification and regression trees