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

SPSC – Advandced Signal Processing (SE)

Professor Horst Cerjak, 19.12.20051

Amir Rahimzadeh 28.11.2007 Fingerprint Features

Fingerprint Features




1 ) Introduction

2 ) Physiology

3 ) Uniqueness of a fingerprint configuration

4 ) Feature Extraction

5 ) Performance




1 ) Introduction




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




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




2 ) Physiology – Fingerprint formation

• Fingerprints begin forming on the fetus 13th 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




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]





• 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





• 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)= Pp =0.2

P(a sequence of N intra-ridge pores)=PpN = 0.2 N

P(a sequence of 20 intra-ridge pores) = 1.05 x 10-14





• 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)





• Rody and Stosz (1999)

• Pmeasured(a sequence of 20 intra-ridge pores) = 0.20120 = = 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




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





• A matter of condition

• Open pores may erroneously be interpreted as ridge endings





• A matter of condition

• Dry skin produces distortions in the image that may be interpreted as pores





[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]





• Presence of pores is not guaranteed

• 2 images of the same finger for different skin conditions




4 ) Feature extraction – Contour Extraction

Wavelet Transform Gabor enhanced image Ridge Contours

- Wavelet response




5 ) Performance

• Hierarchical matching

Level 1: orientation field Level 2: feature location Level 3: pores & ridge contour




5 ) Performance

• Test database:

• 1.640 fingerprint images (Crossmatch 1000ID Sensor)




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]




Thanks for listening!

Documents

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