A Computer Vision Method for the Italian Finger Spelling Recognition

  • Vitoantonio Bevilacqua
  • Luigi Biasi
  • Antonio Pepe
  • Giuseppe Mastronardi
  • Nicholas Caporusso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9227)


Sign Language Recognition opens to a wide research field with the aim of solving problems for the integration of deaf people in society. The goal of this research is to reduce the communication gap between hearing impaired users and other subjects, building an educational system for hearing impaired children. This project uses computer vision and machine learning algorithms to reach this objective. In this paper we analyze the image processing techniques for detecting hand gestures in video and we compare two approaches based on machine learning to achieve gesture recognition.


Image processing Computer vision Machine learning SVM MLP Gaussian Mixture Model Sign language LIS 


  1. 1.
    Alvarez, S., Llorca, D.F.: Spatial Hand Segmentation Using Skin Colour and Background Subtraction, Robesafe Research Group, Department of Automatics, Universidad de Alcala, Madrid, SpainGoogle Scholar
  2. 2.
    Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images, Institute of Information Technology, Department of Statistics, Biostatistics and Informatics, University of Dhaka, BangladeshGoogle Scholar
  3. 3.
    KaewTraKulPong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection, Vision and Virtual Reality group, Department of Systems Engineering, Brunel UniversityGoogle Scholar
  4. 4.
    Piccardi, M.: Background subtraction techniques: a review, Computer Vision Group, Faculty of Information Technology, University of Technology, Sydney (UTS), AustraliaGoogle Scholar
  5. 5.
    Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27, 148–154 (2005)CrossRefGoogle Scholar
  6. 6.
    Gebre, B.G., Wittenburg, P., Heskes, T.: Automatic Sign Language Identification, Max Planck Institute for Psycholinguistics, Nijmegen, Radboud University, NijmegenGoogle Scholar
  7. 7.
    Sign Language in the Intelligent Sensory, Budapest University of Technology and Economics, Department of Automation and Applied Informatics, Budapest, Hungary, Department of Mechatronics, Optics and Instrumentation TechnologyGoogle Scholar
  8. 8.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Thomson Learning, Toronto (2008)MATHGoogle Scholar
  9. 9.
    Otiniano-Rodriguez, K.C., Càmara-Chavez, G., Menotti, D.: Hu and Zernike Moments for Sign Language Recognition, Computing Department, Federal University of Ouro Preto, BrazilGoogle Scholar
  10. 10.
    Yang, M., Kpalma, K., Ronsin, J.: A Survey of Shape Feature Extraction Techniques, IETR-INSA, UMR-CNRS 6164, 35043 Rennes, Shandong University, 250100, Jinan, France, ChinaGoogle Scholar
  11. 11.
    Stokoe, W.C.: Sign language structure: an outline of the visual communication systems of the American deafGoogle Scholar
  12. 12.
    Bevilacqua, V., Filograno, G., Mastronardi, G.: Face detection by means of skin detection. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 1210–1220. Springer, Heidelberg (2008)Google Scholar
  13. 13.
    Bevilacqua, V., Pannarale, P., Abbrescia, M., Cava, C., Paradiso, A., Tommasi, S.: Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression. BMC Bioinform. 13(Suppl. 7), S9 (2012)CrossRefGoogle Scholar
  14. 14.
    Bevilacqua, V.: Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: new tests on an enlarged cohort of polyps. Neurocomputing (2013). doi: 10.1016/j.neucom.2012.03.026. ISSN: 0925-2312 (2012)
  15. 15.
    Bevilacqua, V., Mastronardi, G., Menolascina, F., Pannarale, P., Pedone, A.: A novel multi-objective genetic algorithm approach to artificial neural network topology optimisation: the breast cancer classification problem. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 1958–1965 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vitoantonio Bevilacqua
    • 1
  • Luigi Biasi
    • 1
  • Antonio Pepe
    • 1
  • Giuseppe Mastronardi
    • 1
  • Nicholas Caporusso
    • 2
  1. 1.Dipartimento di Ingegneria Elettrica e dell’InformazionePolitecnico di BariBariItaly
  2. 2.INTACT healthcareBariItaly

Personalised recommendations