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Slovak Fingerprint Individuality model. Zuzana Nemethova, Otokar Gro s ek, Martin Babinec and Marek Sys. Forensic Science Institute of Police Corps & Department of Applied Informatics and Information Technology.

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slovak fingerprint individuality model

Slovak Fingerprint Individuality model

Zuzana Nemethova, Otokar Grosek, Martin Babinec and Marek Sys

Forensic Science Institute of Police Corps

&

Department of Applied Informatics

and Information Technology

8th INTERNATIONAL SYMPOSIUM ON FORENSIC SCIENCES, Sep 26th - 29th 2007, Šamorín-Čilistov, Slovak Republic

en iso iec 17025
EN ISO/IEC 17025
  • In 2002 the ENFSI Board has approved the Policy Commitment on accreditation stating that until the end of the year 2008 all the members should have achieved or be taking steps towards the EN ISO/IEC 17025 compliant accreditation for their laboratory testing activities or other Quality Management standards with equivalent objectives
fsi and accreditation
FSI and accreditation
  • a lot of questions were raised ...
    • Could you find a probability of the individual identification ?
      • is it 99, 9% or 80%, how can you say it is 100%?
    • Could you find a probability of the false identification ?
fingerprint individuality model
Fingerprint Individuality model
  • Galton (1892) – first model, 1/(64x109)
  • Balthazard (1911) – 4 possible events
  • Cummins and Midlo (1943) – pattern factor
  • Roxburgh (1933) – polar coordinate system, correlation among neighboring minutiae, fp quality, position of minutiae configuration relative to the pattern core
  • Osterburg (1977 – 1988) – relative frequencies of minutiae
  • Champod and Margot (1995 – 1996) – 9 minutiae types, 1000 inked fp at 800 dpi, ridge = single pixel line, minutiae density, regional frequencies of minutiae types, relative orientation of minutiae
  • Meagher, Budowle, Ziesig (1999) – first experiment based on inter-comparing records of rolled fingerprints, simulated event - 4 minutiae - probability 1/1027
our model of a fingerprint
Our model of a fingerprint
  • we assume a fingerprint of each finger as a map of 54 x 54 cells
    • in each cell there is possible only one minutiae:
      • ending ridge U
      • bifurcation V
      • dot B (omitted)
our model of a fingerprint1
Our model of a fingerprint
  • we suppose 10 fingers for a person, and thus for each person we have a vector consisting from 10  54  54 = 291600 coordinates
    • 0 stands for non-occurrence of a detail
    • 1,2,3,4 possibilities are for U
    • 5,6,7,8 for V
    • 9 for B (omitted)
  • Thus for each cell we define a random variable, say X, which may take on 10 values. Clearly, these random variables do not have the same probability distribution.
q uestions hypothesizes
Questions / hypothesizes
  • to specify probability distributions for each X
  • test the independence for some specified cells/ regions of cells, using contingency tables; this may enables to determine some coefficients of association
  • to establish some inferences between cells
  • estimation of the main factors (coordinates)
  • using discriminant analysis we will try to find a discrimination function in order to predict some incomplete fingerprints
pilot 1
Pilot 1
  • pilot 1 (2006) we have collected data of 100 fingers (just 10 persons) manually.
    • too few data
    • too time consuming
pilot 1 data
Pilot 1 data

4,12,U3 5,26 U2 5,11,U1 7,28,V2

7,9,V3 9,13,V1 11,31,U2 13,13,U1

14,14,V1 14,30,U4 15,22,V4 15,13,U3

16,11,U1 16,31,U2 17,20,V4 17,14,V1

17,11,U1 17,10,U3 18,22,U4 19,24,V4

19,13,V1 20,22,U3 21,10,U1 22,7,V1

22,8,U2 22,24,V4 22,27,U4 23,23,V4

23,15,U3 24,8,V1 24,24,U2 25,31,U2

25,27,U2 25,25,U2 25,24,U3 25,22,V1

25,14,V1 25,12,U1 26,13,U1 26,15,V1

26,18,V1 26,20,U1 27,27,V2 27,17,V1

28,8,U1 28,9,U1 28,14,B 28,7,V3

29,14,V1 32,10,V3 32,11,U3 32,12,U3

pilot 2
Pilot 2
  • pilot 2 (2007) – we have collected data of 1100 fingers using ISO tester software
    • more data
    • less time consuming
    • FiMiViser – an application to visualize statistical results
pilot 2 data
Pilot 2 - data
  • For 739 samples of all fingers (except the little fingers) we have

0

54

  • 95% correlation in cells

on the following lines:

  • Red: (16,2)  (54,40)
  • Green: (26,2)  (54,31)
  • Yellow: (49,2)  (54,08)
  • angle: 45°

54

our aim is
Our aim is
  • to find a relatively small number of cells << 54  54, which identifies uniquely a person, and calculate the numberrequired for thecoincidence
slide13
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Zuzana Nemethova

Forensic Science Institute of Police Corps

nemethova@keupz.minv.sk

http://ifs.minv.sk/

Otokar Grosek, Martin Babinec, Marek Sys

Slovak University of Technology

Department of Applied Informatics and

Information Technology

[name].[surname]@stuba.sk

http://www.elf.stuba.sk/Katedry/KAIVT/