Importance

• Latent fingerprints are an important source of forensic evidence

• Improving latent fingerprint matching is one of the major goals of FBI’s NGI Program

Houston Cold case: Latent fingerprint found on the victim’s car was used to identify the criminal using FBI’s IAFIS.*

Motivation

• NIST FpVTE: Best matcher achieved rank-1 identification rate of ~99.4% on plain prints [1]

• NIST ELFT EFS II: Best rank-1 identification accuracy for latents is ~63.4% in “lights-out” mode [2]

NIST FpVTE NIST ELFT-EFS

Challenges

Poor Ridge Clarity

Small Friction Ridge area

Complex Background Noise

*http://www.fbi.gov/news/stories/2011/october/print_101411

Proposed Feedback Paradigm

Latent Probe Image Pre-processing Feature Extraction Exemplar Background Database Bottom-up data flow

Matching

Match Score

• Latent matchers [3] [4]: “bottom-up matching”

• Feature extraction for latents is not reliable due to poor ridge clarity and background noise

• We propose to incorporate “top-down information” or feedback from exemplars to improve latent feature extraction

Latent Fingerprint Matching: Accuracy Gain via Feedback from Exemplar Prints Sunpreet S. Arora, Eryun Liu, Kai Cao and Anil. K. Jain

Michigan State University http://www.biometrics.cse.msu.edu

Top-down data flow

Feedback loop for feature refinement

Overlap with other prints

Resorting Candidate List

Latent Matcher

Alignment

Matched Minutiae

Initial Match Score

Initial Matching and Alignment

Exemplar Feature Extraction

Latent Feature Extraction and Refinement

Match Score Computation

• Exemplar features extracted using COTS matcher

Exemplar Exemplar orientation

• Latent matcher matches the latent image to the exemplar (Bottom-up mode)

• Candidate list: Top-K candidate exemplars returned by the latent matcher

• Matched minutiae used to align the latent-exemplar pair

Exemplar Latent

Latent

Exemplar orientation Refined latent feature

• Latent features extracted in Fourier Domain • Refined latent orientation: Latent ridge

orientation closest to the exemplar orientation • Refined latent frequency: Latent ridge

frequency corresponding to the selected orientation

Number of overlapping blocks

Refined latent orientation

Exemplar orientation

Updated Match Score

Initial Match Score

Feedback Orientation

similarity

Feedback Frequency similarity

Feedback Orientation Similarity

Number of overlapping blocks

Refined latent frequency

Exemplar frequency

Feedback Frequency Similarity

Match Score Update

• Product fusion to obtain the updated match score:

The Need for Feedback

• Bottom-up matching suffices for good quality latents

• Feedback should be applied only when needed

Global Criterion Local Criterion

• For each latent query, decide if feedback is needed

• Model the distribution of match scores obtained by matching the latent to the top-K candidate exemplars

• Test for the presence of an upper outlier in the match score probability distribution

• Within each latent, decide regions which need feedback

• Decide exemplar regions which can provide feedback

Latent image Ridge Clarity Map [5]

Gray region needs feedback

Exemplar image Ridge Clarity Map [5]

White region can give feedback

0 0.05 0.1 0.15 0.20

10

20

30

40

50

60

Match Scores

Co

un

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0 0.05 0.1 0.15 0.20

10

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40

50

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70

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90

Match Scores

Co

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Upper outlier present No upper outlier

Feedback needed

No need for feedback

Experimental Evaluation

Match Score Distribution for Latent Query 1

Match Score Distribution for Latent Query 2

• Wrapped the feedback paradigm around the latent matcher in [4]

• Databases: - NIST SD27: 258 operational latents - WVU: 449 latents collected in West Virginia University

• Background: ~32k exemplars • Rank-1 identification accuracy improves by

~9.5% and ~3.5% for NIST SD27 and WVU databases, respectively

Latent Mated exemplar Refined orientation field

Extracted orientation field

NIST SD27

WVU

The retrieval rank of the mated print of the latent improved from 92 to 5 after feedback

References [1] C. Wilson et al., Fingerprint vendor technology evaluation 2003: Summary of results and analysis report, NISTIR7123. [2] M. Indovina et al., ELFT‐EFS Results, NIST Evaluation of Latent Fingerprint Technologies: Extended Feature Sets Evaluation#2. [3] A. Jain and J. Feng, “Latent Fingerprint Matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1):88–100, 2011. [4] A. Paulino et al., “Latent Fingerprint Matching using Descriptor-based Hough Transform”, IEEE Transactions on Information Forensics and Security, 2013. [5] S. Yoon et al., “On Latent Fingerprint Image Quality”, in Proceedings of International Workshop on Computational Forensics, 2012.