The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 145–150, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-145-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 145–150, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-145-2017

  10 May 2017

10 May 2017

MODELLING OF BIOMETRIC IDENTIFICATION SYSTEM WITH GIVEN PARAMETERS USING COLORED PETRI NETS

G. Petrosyan1, L. Ter-Vardanyan2, and A. Gaboutchian3 G. Petrosyan et al.
  • 1Institute for Informatics and Automation Problems of NAS RA, Yerevan, Armenia
  • 2International Scientific - Educational Centre of NAS RA, Yerevan, Armenia
  • 3Moscow State Medical-Stomatological University, 127473 Moscow, Russia

Keywords: Photogrammetry, Colored Petri Nets, Automation, Feature Detection, Biometry, Odontology, Tooth Structure

Abstract. Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.