Guoqiang Li, Bian Yang, Christoph Busch
Norwegian Biometrics Laboratory,
Gjøvik University College, Norway
Norwegian Biometrics Laboratory
A Score‐level Fusion Fingerprint Indexing Approach based on Minutiae Vicinity and Minutia Cylinder‐Code
1
• Fingerprint indexing
• State of art on feature extraction
• Proposed Score‐level fusion approach
• Experimental Results
• Conclusion
Norwegian Biometrics Laboratory
Outline
2
Fingerprint indexing
Enrolment
Feature
extraction
Index
table
Database
The basic structure of
Fingerprint indexing
A list of comparison
scores sorted by descending order
A short list of
candidates
Norwegian Biometrics Laboratory
¾ Fingerprint indexing is a technique to reduce the number of candidate
identities to be considered by the verification algorithm.
Feature extraction
Retrieval
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¾ Global features:
[Liu et al. 2012]
fingerprint image
Orientation field
[Jiang et al. 2006]
¾ Local features:
¾ Other features:
Norwegian Biometrics Laboratory
State of the art on feature extraction
[Li et al. 2006]
4
Delaunay triangulation
Three features are based on the geometry
of the triangle;
Six features are extracted from the ridges
associated with three minutiae.
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State of the art on feature extraction
[Ross and Mukherjee. 2007]
5
State of the art on feature extraction
3D local structure
Computing a numerical
value for each cube.
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Minutia Cylinder‐Code (MCC)
2D local structure
[Cappelli et al. 2011]
6
Proposed feature extraction
Each triangle is represented by a 9‐D feature vector:
¾
4 geometric features:
Norwegian Biometrics Laboratory
Delaunay triangulation
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Proposed feature extraction
¾
¾
3 features based on the minutia direction info.;
Norwegian Biometrics Laboratory
¾
1 feature is the average value of ridge curvatures from three minutiae;
1 feature is based on the ridge density around the
location of the minutia neighbors;
[Tomasz and Wieclaw, 2011]
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¾ Design a new indexing method using this feature vector;
¾ A score-level fusion approach by by combing the Minutiae Vicinity (MV) indexing
method with the minutiae cylinder-code (MCC) indexing method;
Norwegian Biometrics Laboratory
Proposed a score‐level fusion method
Structure of proposed approach
9
Training set
9‐D feature vector
from trianing set
K‐means
algorithm
0
0
1
0
1
. . .
A set of feature
vectors
. . .
Feature
extraction
1
. . .
Each feature vector
0
0
0
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Minutiae Vicinity based indexing method
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Proposed a score‐level fusion method
Reference samples
Each subject will be represented
by a K-dimension binary string.
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MV-Index space will be represented by a matrix M whose size is R * K, where R is
the number of subjects enrolled in the database, K is the number of clusters.
A example of MV-Index space
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Proposed a score‐level fusion method
Feature
extraction
3
56
. . .
A set of feature
vectors
Each feature
vector
. . .
0
2
. . .
0
1
0
. . .
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Retrieval in minutiae vicinity (MV) based index Space
1
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Performance measures
Pre‐selection error : error that occurs when the corresponding enrolment template is not in the preselected subset of candidates when a sample from the same biometric characteristic on the same user is given;
Penetration rate : measure of the average number of pre‐selected templates as a fraction of the total number of templates;
The number of
candidates: 10
Enrolled 1,000 subjects
Database
Penetration rate: 10/1,000 =1%
If 10 probes are outside of the candidates list, pre‐selection error: 10/100 = 10%;
If 5 probes are outside of the candidates list, pre‐selection error: 5/100 = 5%;
The number of
candidates: 50
Norwegian Biometrics Laboratory
From ISO/IEC 19795‐1
Penetration rate: 50/1,000 =5%
100 probe samples
Each probe sample
Low peneration rate, and low pre‐selection error
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20
15
10
5
[Ross et al. 2007]
40.04
43.03
45.97
48.75
MV‐Index
12
17.38
26
48.5
MCC‐Index
<1
2.94
7
16.5
MV‐MCC fusion
<1
<1
1.19
8.9
Pre‐selection Error Rate(%)
Penetration
Rate(%)
Performance evaluation of MV-MCC fusion, MCC-index,
MV-Index and [Ross et al. 2007] on database FVC_2004_DB1.
20
15
10
5
[Ross et al. 2007]
40.79
43.61
46.45
49.34
MV‐Index
10
18.25
32.33
45.75
MCC‐Index
1.8
4.93
15
29.5
MV‐MCC fusion
<1
<1
2.4
9.75
Pre‐selection Error Rate(%)
Database preparation
Penetration
Rate(%)
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Experimental results
Performance evaluation of MV-MCC fusion, MCC-index,
MV-Index and [Ross et al. 2007] on database FVC_2004_DB2.
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Database preparation
Performance evaluation of MV-MCC fusion,
MCC-index, MV-Index on database FVC_2004_DB1_a.
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Experimental results
15
Performance evaluation of MV-MCC fusion, MCC-index, MV-Index on database FVC_2004_DB2_a.
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Experimental results
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• Extracted a feature vector including 9 components based on the
minutiae vicinity;
• Developed a new indexing method using this feature vector;
• Proposed a scole‐level fusion approach by combing this new
method with minutia cylinder‐code (MCC) indexing method;
• Future works:
•
•
Improve the performance of indexing method based on the minutiae
vicinity;
Consider to set‐up the fusion on feature level;
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Conclusion
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•
[VeriFinger] “Verifinger,” http://www.neurotechnology.com/verifinger.html, Accessed: 2013‐10‐30.
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[Quinn and Grother. 2012 ] George W Quinn and Patrick Grother, “The one‐tomany multi‐modal fusion challenge,” in Biometrics (ICB), 2012 5th IAPR International Conference on.
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[A reviewer. 2006 ] A Review of the FBI’s Handling of the Brandon Mayfield Case. http://www.justice.gov/oig/special/s0601/exec.pdf. •
[Liu et al. 2012] Reference: Manhua Liu, Pew‐Thian Yap: Invariant representation of orientation fields for fingerprint indexing. Pattern Recognition 45(7): 2532‐2542 (2012).
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[Li et al. 2006] Jun Li, Wei‐Yun Yau, and Han Wang, “Fingerprint indexing based on symmetrical measurement,” in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. IEEE, 2006, vol. 1, pp. 1038–1041.
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[Ross and Mukherjee. 2007] Arun Ross and Rajiv Mukherjee, “Augmenting ridge curves with minutiae triplets for fingerprint indexing,” in Proceedings of SPIE Conference on Biometric Technology for Human Identification IV, 2007, vol. 6539, p. 65390C.
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[Cappelli et al. 2011] Raffaele Cappelli, Matteo Ferrara, and Davide Maltoni, “Fingerprint indexing based on minutia cylinder‐code,” Pattern Analysis and Machine Intelligence, IEEE Transactionson, vol. 33, no. 5, pp. 1051–1057, 2011.
•
[Tomasz and Wieclaw, 2011] Orczyk, Tomasz, and Lukasz Wieclaw. "Fingerprint ridges frequency." Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on. IEEE, 2011.
•
[Jiang 2006] Jiang, Xudong, Manhua Liu, and Alex C. Kot. "Fingerprint retrieval for identification." Information Forensics and Security, IEEE Transactions on 1.4 (2006): 532‐
542.
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References
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THANKS
• The Norwegian Information Security Laboratory
Norwegian Biometrics Laboratory
• FIDELITY Project;
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