1. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 1 Amir Rahimzadeh 28.11.2007 Fingerprint Features

2. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 2 Amir Rahimzadeh 28.11.2007 Fingerprint Features 1 ) Introduction 2 ) Physiology 3 ) Uniqueness of a fingerprint configuration 4 ) Feature Extraction 5 ) Performance

3. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 3 Amir Rahimzadeh 28.11.2007 Fingerprint Features 1 ) Introduction

4. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 4 Amir Rahimzadeh 28.11.2007 Fingerprint Features 1 ) Introduction “Most of fingerprint identification systems (like AFIS)‏ rely on minutiae (Level 1&2) only. While this information is sufficient for matching fingerprints in small databases, it is not discriminatory enough to provide good results on large collections of fingerprint images.“ [M. Ray, P. Meenen, R. Adhami - “A Novel Approach to Fingerprint Pore Extraction“, IEEE, Mar. 2005] AFIS...Automatic Fingerprint Identification System

5. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 5 Amir Rahimzadeh 28.11.2007 Fingerprint Features 1 ) Introduction – fragment of 2 different Fingerprints –both show a bifurcation at the same location –Examination based on Level 1&2 features – match –In combination with Level 3 features (e.g. relative pore position) – no match

6. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 6 Amir Rahimzadeh 28.11.2007 Fingerprint Features 2 ) Physiology – Fingerprint formation Fingerprints begin forming on the fetus 13 th week of devellopment Bumps or ridge units are fusing together as they grow forming ridges Each ridge unit contains a pore which originates from a sweat gland from the dermis Pores are only found on ridges not in valleys sweat gland...Schweissdrüse

7. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 7 Amir Rahimzadeh 28.11.2007 Fingerprint Features 2 ) Physiology – Some facts typical fingerprint: 150 ridges A ridge ~ 5 mm long contains appr. 10 ridge units Ridge width: ~ 0.5 mm Average number of pores / cm ridge ~ 9-18 pores Pores do not disappear, move or generate over time [Ashbaugh, D., Quantitative-Qualitative Friction Ridge Analysis, 1999, CRC Press] [Locard, Les pores et l'identification des criminals, Biologica, vol.2, pp. 257-365, 1912]

8. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 8 Amir Rahimzadeh 28.11.2007 Fingerprint Features 3 ) Uniqueness of a fingerprint configuration Ashbaugh model (1982) Assumptions Ridge units occur regularly along a ridge Position of a pore on a ridge unit is a random variable Independence between ridge units

9. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 9 Amir Rahimzadeh 28.11.2007 Fingerprint Features 3 ) Uniqueness of a fingerprint configuration Ashbaugh model (1982) 5 general areas where a pore may occur on the ridge unit Under the assumption of independence of ridge units P(pore in A)=P(pore in B)=...=P(pore in E)= P p =0.2 P(a sequence of N intra-ridge pores)=P p N = 0.2 N P(a sequence of 20 intra-ridge pores) = 1.05 x 10 -14

10. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 10 Amir Rahimzadeh 28.11.2007 Fingerprint Features 3 ) Uniqueness of a fingerprint configuration Rody and Stosz (1999) Estimated uniqueness of a sequence of intra-ridge pores based on measurements of real fingerprints (3748 distance measures)‏ Most common distance: 13 pixels (0.3 mm)‏

11. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 11 Amir Rahimzadeh 28.11.2007 Fingerprint Features 3 ) Uniqueness of a fingerprint configuration Rody and Stosz (1999) P measured (a sequence of 20 intra-ridge pores) = 0.201 20 = = 1.16 x 10 -14 Assuming typical pore diameter of 5 pixels (115.5µm) allowing a displacement of 3 pixels (69.3µm)‏ P(a sequence of 20 ridge independent pores) = = 5.186 x 10 -8

12. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 12 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Pore extraction A matter of resolution Same fingerprint at different image resolutions: 380 ppi (Identix 200DFR) (b) 500 ppi (Cross Match ID500) (c) 1000 ppi (Cross Match ID1000)‏ 250-300 ppi minimum resolution for level 1 & level 2 features 500 ppi FBI standard for AFIS 1000 ppi minimum for extracting level 3 features

13. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 13 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Pore extraction A matter of condition Open pores may erroneously be interpreted as ridge endings

14. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 14 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Pore extraction A matter of condition Dry skin produces distortions in the image that may be interpreted as pores

15. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 15 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Pore extraction [Anil K. Jain, Yi Chen, Meltem Demirkus: “Pores and Ridges: High Resolution Fingerprint matching using level 3 features“, IEEE Transactions on pattern analysis and machine intelligence, Vol.29, No.1, Jan. 2007]

16. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 16 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Pore extraction Presence of pores is not guaranteed 2 images of the same finger for different skin conditions

17. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 17 Amir Rahimzadeh 28.11.2007 Fingerprint Features 4 ) Feature extraction – Contour Extraction Wavelet Transform Gabor enhanced image Ridge Contours - Wavelet response

18. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 18 Amir Rahimzadeh 28.11.2007 Fingerprint Features 5 ) Performance Hierarchical matching Level 1: orientation field Level 2: feature location Level 3: pores & ridge contour

19. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 19 Amir Rahimzadeh 28.11.2007 Fingerprint Features 5 ) Performance Test database: 1.640 fingerprint images (Crossmatch 1000ID Sensor)

20. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 20 Amir Rahimzadeh 28.11.2007 Fingerprint Features Referenzen [M. Ray, P. Meenen, R. Adhami - “A Novel Approach to Fingerprint Pore Extraction“, IEEE, Mar. 2005] [Ashbaugh, D., Quantitative-Qualitative Friction Ridge Analysis, 1999, CRC Press] [Locard, Les pores et l'identification des criminals, Biologica, vol.2, pp. 257-365, 1912] [Anil K. Jain, “Pores and Ridges: High Resolution Fingerprint matching using level 3 features“, IEEE ransactions on pattern analysis and machine intelligence, Vol.29, No.1, Jan. 2007]

21. SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, 19.12.2005 21 Amir Rahimzadeh 28.11.2007 Fingerprint Features Thanks for listening!