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

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

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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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 , 1912]

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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)‏

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh Fingerprint Features 3 ) Uniqueness of a fingerprint configuration Rody and Stosz (1999) P measured (a sequence of 20 intra-ridge pores) = = = 1.16 x 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) = = x 10 -8

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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)‏ ppi minimum resolution for level 1 & level 2 features 500 ppi FBI standard for AFIS 1000 ppi minimum for extracting level 3 features

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

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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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]

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

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

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

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

SPSC – Advandced Signal Processing (SE) Professor Horst Cerjak, Amir Rahimzadeh 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 , 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]

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