Advertisement

Person Identification Technique Using RGB Based Dental Images

  • Soma DattaEmail author
  • Nabendu Chaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9339)

Abstract

Dental signature captures information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work. However, this is complicated as dental features change with time. In this paper, we proposed a new, safe and low cost dental biometric technique based on RGBimages. It uses three phases: image acquisition with noise removal, segmentation and feature extraction. The key issue that makes our approach distinct is that the features are extracted mainly from incisor teeth only. Thus the proposed solution is low cost besides being safe for human.

Keywords

HSI color format Wiener filtering Opening Watershed Snake 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Choorat, P., Chiracharit, W., Chamnongthai, K..: A single tooth segmentation using structural orientations and statistical textures. In: Biomedical Engineering International Conference (BMEiCON), 2011, pp. 294–297. IEEE (2012)Google Scholar
  2. 2.
    Chen, H., Jain, A..: Dental biometrics: alignment and matching of dental radiographs. In: Application of Computer Vision WACV/MOTIONS 2005, vol. 1, pp. 316–321. IEEE (2005)Google Scholar
  3. 3.
    Siltanen, S., et al.: Statistical inversion for medical x-ray tomography with few radiographs: I. General theory. Physics in medicine and biology 48(10), 1437–1464 (2003)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Ito, K., et al.: A fingerprint recognition algorithm using phase-based image matching for low-quality fingerprints. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. 2–33 (2005)Google Scholar
  6. 6.
    Jea, T.Y., Govindaraju, V.: A minutia-based partial fingerprint recognition system. Pattern Recognition 38(10), 1672–1684 (2005). ElsevierCrossRefGoogle Scholar
  7. 7.
    Javad, H., Fatemizadeh, E.: Biometric identification through hand geometry. In: The International Conference on Computer as A Tool, EUROCON, 2005, vol. 2, pp. 1011–1014. IEEE (2005)Google Scholar
  8. 8.
    Jain, A.: Matching of dental X-ray images for human identification. Pattern recognition 37(7), 1519–1532 (2004)CrossRefGoogle Scholar
  9. 9.
    Shah, S., et al.: Automatic tooth segmentation using active contour without edges. In: Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, 2006. IEEE (2006)Google Scholar
  10. 10.
    Nomir, O., Mottaleb, A..: Human identification from dental X-ray images based on theshape and appearance of the teeth. IEEE Transactions on Information Forensics and Security 2(2) (2007)Google Scholar
  11. 11.
    Labial Teeth and Gingiva Image Database, Color Imaging Laboratory, Department of Optics University of Granada, Spain, Set 1: undried oral cavity, Part 1/1Google Scholar
  12. 12.
    Gonzalez, R.C., Richard, R.E.: Woods, digital image processing, 2nd edn. Prentice Hall Press (2002). ISBN 0-201-18075-8Google Scholar
  13. 13.
    Liu, Y., Sargur, N.: Document image binarization based on texture features. IEEE Transactions Pattern Analysis and Machine Intelligence 19(5), 540–544 (1997)CrossRefGoogle Scholar
  14. 14.
    Kass, M.: Snakes:Active Contour Models. International Journal of Computer Vision, 321–331 (1988)Google Scholar
  15. 15.
    Roerdink, J., Meijster, A.: The Watershed Transform – Definition Algorithm and parallelization Strategies. Foundamental Informatica, vol. 41, pp. 187–228. IOS Press (2001)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of CalcuttaKolkataIndia

Personalised recommendations