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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
t
0 0.05 0.1 0.15 0.20
10
20
30
40
50
60
70
80
90
Match Scores
Co
un
t
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.