L t a ent Fingerprint Image Qua

( Q) LFI

An il Ja Y in, Soweon Yoon, K ia Cao, and Eryun 

Michigan State University

biometrics.cse.msu.edu
Image Quality

• Image quality indicates “perceived” image

degradation degradation with/without with/without relation relation to a ref

ref

image

• Factors Factors affecting affecting image quality quality

– Sharpness, contrast, noise, distortion, resolut

dynamic range,..

Standard De finition 

• Quality assessment

– Qualitative (Good/bad/ugly)

vs.

Quantitative (SNR)
Fingerprint Image Quality

• Prediction of AFIS performance for feature extraction and 

– “Perceived” fingerprint image quality may not necessarily co

AFIS performance

Good quality fingerprint (NFIQ* = 1) Poor quality fingerprint (N

* NIST Fingerprint Image Quality; value is from 1 (highest quality) to 5 (lowe
Latent Quality Assessment by Exa

• ACE‐V methodology• Examiner determines latent value in analysis 

– Value for Individualization (VID)– Value for Exclusion Only (VEO)

– No Value (NV)

• Only VID or VEO latents are searched via AFIS• Concern: Reliability and consensus of value

determination by latent examiners

– Visual perception, expertise of examiners, worklo
Value vs. Identification Identification Rat

Value for

Identification

Value for

Exclusion Only 

NIST SD27* (258 latents) 210 41 WVU (449 latents) 370 74 Rank‐1 ID Rate 491 (85%) 46 (40%) 

Rank‐100 ID Rate 525 (91%) 72 (63%) 

A significant number of VEO or NV latents can be successfully identifi

Identification rate is obtained by combining multiple AFIS; if the mate of a la

within rank m by any of the AFIS, it is considered as a successful match with

* Hicklin et al., “Latent Fingerprint Quality: A Survey of Examiners”, Journal of Forensic Identific
Reliability of Examiners’ Value Determ

• NV and VEO latents successfully matched at rank 1 

‘No Value’ (NV) Latent ‘Value for Exclusion Only’ (V
Consensus of Examiners’ Value Determ

• Each latent was evaluated by an average of 23 exam• Unanimous Unanimous value determination determination was made on only 4

latents (either VID or not‐VID)

Unanimous value determin

Unanimous value determination

Ulery et al., “Repeatability and reproducibility of decisions by latent fingerprint examiners”, PLO
Goals of Our Study

• Provide an objective measure of latent qu

avoid misleading conclusions in subjective evaluation

• Identify latents which can be processed in

“Lights‐out” mode
Tenprint Quality vs. Latent Qu

• Tenprint quality assessment

– Clean background; central part of the finge– Usually defined in terms of clarity of ridge 

valley structures

• Latent quality assessment

– Severe background noise, off‐center finge

position, skin distortion, etc.

– Local ridge clarity measures alone cannot 

determine latent quality
Latent Quality Quality Definition

• Features

– Local ridge clarity in presence of severe bac

noise

– Vicinity of good quality ridge areas– Position of mark– Minutiae reliability

• Matcher‐independent vs. matcher‐depe
• 

• 

Matcher Matcher‐Independent Independent vs. Matcher Matcher‐De

De

Matcher‐Independent Quality Measure

– A latent is considered VID if any one of the 

can successfully successfully retrieve retrieve its mate from a re

database within the candidate list

Matcher‐Dependent Quality Measure

– A latent is considered VID if a specific AFIS 

successfully successfully retrieve retrieve its mate from a refere

database within the candidate list
AFIS Interoperability

Retrieval Rank AFIS 1 AFIS 2 AFIS 3

Proprietary Minutiae 32 561 22

Markup Minutiae 31,997 156 
x =

FFT

Local Ridge Quality

• Ridge Clarity

Local Block Gaussian

Mask

• Ridge Continuity

Continuous blocks Discontinuous blocks

Power 

Orientation Frequency 
Local Ridge Quality
Diction

...

High Quality

High Quality Fingerprints Minutiae Patches (48x48)

Minutiae Reliability: Learni

• Minutiae patch dictionary learning
Minutia Patch Dictionary E

P D = {dm|m = 1

Qm = max {SSIM(P, {SSIM(P, 

Minutiae Reliability

Latent Fingerprint

Reliability of patch P (Qm) is define

Structural tructural SIMilarity ilarity (SSIM) betwee

betwee

closest dictionary element dm:

Wang et al., Image Quality Assessment: From Error Visibility to Structural Similarity, 
Reference Reference Point Detection

Orientation Field Curvature map

(Reconstructed from minutiae)

Weight 

• Reference point is determined as the point where the curvature is max
Quality Score

• For each triangle T

3 

QT r i  Q i Q 

m 

j 1 

Qri: Average ridge quaQmij: Reliability of the Wmij: Weight based o• Quality score of a 

LFIQ  

i 

N: Number of trian

trian
Experiments

• Latent Databases (707 latents)

– NIST SD27: 258 l t a ents

– WVU Latent DB: 449 latents

• E l D t b (31 xemplar Dtb a a ases (31,997 rolled pri t ) n s

– NIST SD27: Mated 258 rolled prints– WVU: Mated 449 rolled prints; prints; 4,290 rolled prints– NIST SD14: 27,000 rolled prints

• Matcher: Matcher: Three COTS matchers matchers• Matcher‐independent approach• Using markup minutiae for preliminary study
25 

30 

707 Lat

15 20 

entage (%) 

5 

10

Perce 

0 50 100 150 200 

0 5 

LFIQ 

Quality Index = 1 30 50 80 

LFIQ Distribution
90 

100 

70 80 

te (%) High Quality

50 60 

tification Ratg y

(High matching accuracy expe

30 40 

Rank-1 Ident 

10 20 

R 

Ideal Case COTS Tenprint Quality MeasuLFIQ 

Low Quality

(Low matching accuracy

expected)

0 

10 20 30 40 50 60 70 80 9

Quality Index 

Performance: Rank‐1 Identificatio
Successful Prediction

• Quality Index 74 (LFIQ = 26); Mate retrieved at rank 1; examiner labeled 

Latent Mated Rolled Pri
Latent Mated Rolled Print

• Quality Index 92 (LFIQ = 51)

• Value determination by examiner: VID

• Retrieval rank of the mate: 600

Unsuccessful Prediction

• High quality, but low matching performance
Quality Quality of Exemplars Exemplars in Latent Ma

Ma

• NFIQ = 5• Is this still adequate

for l taent mat hicng?
Mated rolled prin

Latent print

Quality Quality of Exemplars Exemplars in Latent Ma

Ma

AFIS can successfully match the pair

• May need to relax quality measure for rolled prints in lat
Conclusions

• Latent fingerprint image quality (LFIQ) assessm

cruci l a for properly determining latent value a

evidence

• LFIQ is an objective measure of latent quality

– Distinguish latents that can be processed in “lights‐

– Complement latent examiners’ value determination

• Investigated various features (ridge quality, mi

quality, position of mark) in defining LFIQ
Thank you