Transcript Splitting the Unit Delay - Graz University of Technology

SPSC – Advandced Signal Processing (SE)
Fingerprint Features
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
1
SPSC – Advandced Signal Processing (SE)
1 ) Introduction
2 ) Physiology
3 ) Uniqueness of a fingerprint configuration
4 ) Feature Extraction
5 ) Performance
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
2
SPSC – Advandced Signal Processing (SE)
1 ) Introduction
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
3
SPSC – Advandced Signal Processing (SE)
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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
4
SPSC – Advandced Signal Processing (SE)
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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
5
SPSC – Advandced Signal Processing (SE)
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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
6
SPSC – Advandced Signal Processing (SE)
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]
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
7
SPSC – Advandced Signal Processing (SE)
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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
8
SPSC – Advandced Signal Processing (SE)
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)= 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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
9
SPSC – Advandced Signal Processing (SE)
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)
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
10
SPSC – Advandced Signal Processing (SE)
3 ) Uniqueness of a fingerprint configuration
• 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
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
11
SPSC – Advandced Signal Processing (SE)
4 ) Feature extraction – Pore extraction
• A matter of resolution
Same fingerprint at different image resolutions:
380 ppi (Identix 200DFR)
•
•
•
250-300 ppi
500 ppi
1000 ppi
(b) 500 ppi (Cross Match ID500) (c) 1000 ppi (Cross Match ID1000)
minimum resolution for level 1 & level 2 features
FBI standard for AFIS
minimum for extracting level 3 features
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
12
SPSC – Advandced Signal Processing (SE)
4 ) Feature extraction – Pore extraction
• A matter of condition
•
Open pores may erroneously
be interpreted as ridge endings
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
13
SPSC – Advandced Signal Processing (SE)
4 ) Feature extraction – Pore extraction
• A matter of condition
•
Dry skin produces distortions in
the image that may be interpreted as
pores
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
14
SPSC – Advandced Signal Processing (SE)
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]
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
15
SPSC – Advandced Signal Processing (SE)
4 ) Feature extraction – Pore extraction
• Presence of pores is not guaranteed
•
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
2 images of the same finger for different skin conditions
28.11.2007
Fingerprint Features
16
SPSC – Advandced Signal Processing (SE)
4 ) Feature extraction – Contour Extraction
Wavelet Transform
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
Gabor enhanced image
- Wavelet response
28.11.2007
Ridge Contours
Fingerprint Features
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SPSC – Advandced Signal Processing (SE)
5 ) Performance
• Hierarchical matching
Level 1: orientation field
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
Level 2: feature location
28.11.2007
Level 3: pores & ridge contour
Fingerprint Features
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SPSC – Advandced Signal Processing (SE)
5 ) Performance
•
Test database:
•
1.640 fingerprint images
(Crossmatch 1000ID Sensor)
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
19
SPSC – Advandced Signal Processing (SE)
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]
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
20
SPSC – Advandced Signal Processing (SE)
Thanks for listening!
Amir Rahimzadeh
Professor
Horst Cerjak, 19.12.2005
28.11.2007
Fingerprint Features
21