Partial Fingerprint Registration for
Forensics using Minutiae-Generated
Orientation Fields
Ram P. Krish1 , Julian Fierrez1 ,
Daniel Ramos1 , Javier Ortega-Garcia1 , Josef Bigun2
1
ATVS - Biometric Recognition Group.
Universidad Autonoma de Madrid, Spain
2
Intelligent Systems Lab,
University of Halmstad, Sweden
March, 2014
2nd
International Workshop on Biometrics and Forensics
Valletta, Malta
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Outline
Introduction
X Motivation
X Problem statement
Methods
X Minutiae to Orientation Field
X Correlation based pre-alignment (registration)
Experiments
X Database and Results
Discussion
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Introduction
• Motivation
X Comparing a partial fingerprint against a full fingerprint is a challenging problem.
X Latent fingerprints lifted from crime scenes are mostly partial fingerprints in nature.
X Minutiae based representation scheme is the most widely adapted representation scheme by
many fingerprint matching systems.
X Strict analogy with forensic friction ridge analysis.
X Minutiae based decision is accepted as proof of identity legally by courts in almost all
countries around the world.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Introduction
† Problem statement
X Automated minutiae based matching systems usually expects the size of minutiae set
between query and reference is approximately the same.
X It will be advantageous if we can reduce the minutiae search space of full fingerprint with
respect to the partial fingerprint while comparison.
How to go about reducing the search space in
full fingerprint minutiae set w.r.t that of partial fingerprint?
Robust pre-alignment using Orientation Field
? Orientation Field reconstructed from minutiae set
? Similarity measure based on normalized correlation
This registration obtains extra information that can augment any
minutiae based matcher.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
• Minutiae to Orientation Field
X Orientation field (OF) reconstructed from minutiae.
(J Feng, AK Jain, “Fingerprint Reconstruction: From Minutiae to Phase”,
TPAMI, Feb 2011 )
X Minutiae generated OF is very similar to actual OF.
X Reconstructed OF least affected due to noise in the fingerprint image.
X Ability to reconstruct OF with only few minutiae (even if only 60% of minutiae is present).
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
Minutiae to Orientation Field
X An example of latent and tenprint OF reconstructed from its minutiae sets.
(example from NIST SD-27 database)
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
Minutiae to Orientation Field
X For this example, the region in the tenprint that is to be found after registration.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
• Correlation based pre-alignment
X Orientation tensors for both latent and tenprint.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
Correlation based pre-alignment
X Correlating latent tensors in tenprint tensors.
X To compensate for rotation alignment, latent tensors are rotated in range [−45◦ , 45◦ ]
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Partial Fingerprint Registration for Forensics
Methods
Correlation based pre-alignment
X The region where latent pattern is identified in tenprint,
location with maximum magnitude and minimum phase value in correlated result.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Methods
Correlation based pre-alignment
X Minutiae subset of tenprint selected by our registration algorithm, inside a circular region
with radius defined by half the diagonal of the bounding box of latent pattern.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Experiments
• Database
NIST Special Database (SD) 27
X Publicly available forensic fingerprint database.
X Broadly classified into 1) Ideal and 2) Matched minutiae database.
X Ideal databases
X Ideal latent consists of all minutiae manually extracted by forensic examiner.
X Ideal impression consists of all minutiae extracted by AFIS, followed by manual
validation by examiner.
X Matched databases
X Only contains those minutiae that are in common between the latent and its
mated impression template.
X There is a one-to-one correspondence between latent and its mate in matched
templates.
X Minutiae attribute consists of only location and orientation.
X No type information available as minutiae attribute.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Experiments - Database
• NIST SD-27
X Contains 258 latent fingerprint images and 258 mated tenprint images.
X Latent fingerprint images are of varying qualities.
X Classification based on subjective quality of latent fingerprint image:
X Good - containing 85 images
X Bad - containing 88 images
X Ugly - containing 85 images
X Classification based on total number of minutiae (n) in latent minutiae set:
X Large - containing 83 images (n > 21)
X Medium - containing 82 images (13 < n < 22)
X Small - containing 93 images (n < 14)
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Experiments - Protocol
† Performance measurement
X Registration algorithm finds a subregion in tenprint
that best aligns latent and tenprint OF.
X Based on this registration, a subset of minutiae from
tenprint minutiae set is chosen.
X The ground truth (matched) minutiae set in NIST SD-27
can be used to check how many of mated minutiae are present in this new subset.
X We report the performance of our registration algorithm
in terms of percentage of mated minutiae present in the new subset generated.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Results
Subjective Classification
X In average case scenario (without quality classification), 89% of entire database contains at
least 75% of the mated minutiae in the new search space generated by our registration
algorithm.
Percent of database correctly identified
100
95
90
85
80
75
70
65
60
55
0
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Average
Good
Bad
Ugly
20
40
60
80
100
Minimum percent of matched minutiae in new search space
Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Results
Quantitative Classification
Percent of database correctly identified
100
95
90
85
80
75
70
65
60
55
0
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Average
Large
Medium
Small
20
40
60
80
100
Minimum percent of matched minutiae in new search space
Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
Discussion
Summary
Performace of registration algorithm for selected threshold, all values in %
Threshold
Average
Good
Bad
Ugly
Large
Medium
Small
75
80
85
90
95
100
89
88
87
85
80
79
100
100
100
99
97
95
85
85
84
84
82
80
82
79
77
70
62
60
97
97
97
97
97
94
94
94
93
90
84
82
78
76
74
69
63
62
Using our registration algorithm, we can obtain extra information that can augment
minutiae based matcher by reducing the search space for Good quality latents.
The deteriorated performance in case of Bad and Ugly classification is due to few number of
minutiae and the degraded quality of estimated OF.
Future work : A detailed analysis on how this registration algorithm can be incorporated to
improve the identification of minutiae-based matcher.
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics
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Biometric Recognition Group - ATVS-UAM
Partial Fingerprint Registration for Forensics