Partial Fingerprint Registration for Forensics using … 27th...Partial Fingerprint Registration for Forensics using Minutiae-Generated Orientation Fields Ram P. Krish 1, Julian Fierrez

Partial Fingerprint Registration forForensics using Minutiae-Generated

Orientation Fields

Ram P. Krish1, Julian Fierrez1,Daniel Ramos1, Javier Ortega-Garcia1, Josef Bigun2

1ATVS - Biometric Recognition Group.Universidad Autonoma de Madrid, Spain

2Intelligent Systems Lab,University of Halmstad, Sweden

March, 2014

2nd International Workshop on Biometrics and Forensics

Valletta, Malta

1 / 18 Biometric Recognition Group - ATVS-UAM Partial Fingerprint Registration for Forensics

Outline

IntroductionX MotivationX Problem statement

MethodsX Minutiae to Orientation FieldX Correlation based pre-alignment (registration)

ExperimentsX Database and Results

Discussion


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 bymany 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 allcountries around the world.


Introduction

† Problem statement

X Automated minutiae based matching systems usually expects the size of minutiae setbetween query and reference is approximately the same.

X It will be advantageous if we can reduce the minutiae search space of full fingerprint withrespect to the partial fingerprint while comparison.

How to go about reducing the search space infull 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 anyminutiae based matcher.


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).


Methods

Minutiae to Orientation Field

X An example of latent and tenprint OF reconstructed from its minutiae sets.(example from NIST SD-27 database)


Methods

Minutiae to Orientation Field

X For this example, the region in the tenprint that is to be found after registration.


Methods

• Correlation based pre-alignment

X Orientation tensors for both latent and tenprint.


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◦]


Methods


X The region where latent pattern is identified in tenprint,location with maximum magnitude and minimum phase value in correlated result.


Methods


X Minutiae subset of tenprint selected by our registration algorithm, inside a circular regionwith radius defined by half the diagonal of the bounding box of latent pattern.


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 itsmated impression template.

X There is a one-to-one correspondence between latent and its mate in matchedtemplates.

X Minutiae attribute consists of only location and orientation.X No type information available as minutiae attribute.


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 imagesX Bad - containing 88 imagesX 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)


Experiments - Protocol

† Performance measurement

X Registration algorithm finds a subregion in tenprintthat best aligns latent and tenprint OF.

X Based on this registration, a subset of minutiae fromtenprint minutiae set is chosen.

X The ground truth (matched) minutiae set in NIST SD-27can be used to check how many of mated minutiae are present in this new subset.

X We report the performance of our registration algorithmin terms of percentage of mated minutiae present in the new subset generated.


Results

Subjective Classification

X In average case scenario (without quality classification), 89% of entire database contains atleast 75% of the mated minutiae in the new search space generated by our registrationalgorithm.

0 20 40 60 80 10055

60

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100

Minimum percent of matched minutiae in new search space

Pe

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Average

Good

Bad

Ugly


Results

Quantitative Classification

0 20 40 60 80 10055

60

65

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75

80

85

90

95

100

Minimum percent of matched minutiae in new search space

Pe

rce

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Average

Large

Medium

Small


Discussion

Summary

Performace of registration algorithm for selected threshold, all values in %

Threshold Average Good Bad Ugly Large Medium Small

75 89 100 85 82 97 94 78

80 88 100 85 79 97 94 76

85 87 100 84 77 97 93 74

90 85 99 84 70 97 90 69

95 80 97 82 62 97 84 63

100 79 95 80 60 94 82 62

Using our registration algorithm, we can obtain extra information that can augmentminutiae 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 ofminutiae and the degraded quality of estimated OF.

Future work : A detailed analysis on how this registration algorithm can be incorporated toimprove the identification of minutiae-based matcher.



Documents

Partial Fingerprint Registration for Forensics using … 27th...Partial Fingerprint Registration for Forensics using Minutiae-Generated Orientation Fields Ram P. Krish 1, Julian Fierrez