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ICDP 2011
1. ICDP 2011
Latent Fingerprint Segmentation using
Ridge Template Correlation
Nathan Short, A. Lynn Abbott, Michael S. Hsiao,
Edward A. Fox
Virginia Tech
October 11th, 2011
2. Motivation
Large sample of good
quality features
Supervised acquisition of
sample fingerprint
Few good quality
features for matching
Low quality
Low fingerprint surface
area
Rolled/Plain Fingerprints Latent Fingerprints
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5. Motivation (cont.)
Automated Fingerprint Identification Systems
(AFIS)
Minutia based
Aimed towards Plain/Rolled fingerprint matching
Large sample size
Latent fingerprints continue to be encoded
manually
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6. Motivation (cont.)
Latent matching
Recent work has included additional features in
matching process [Jain and Feng]
minutiae, core points, ridge flow, local quality, ridge
wavelength, and others
matching results much improved over minutia-only based
methods
All features are extracted manually from latent prints
for matching
Quality is subjective
1/29/2015
8. Traditional Segmentation
Normalize Image
Min-max
Remove areas with low variance
Compute Gradient Image
Approximate first derivative of normalized image by convolving
with Sobel filter
Threshold based on average magnitude of gradient
within local blocks
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𝛻 𝐼 =
1
𝑛2
𝑖,𝑗 ∈ 𝐵
𝐺 𝑥 𝑖,𝑗
2
+ 𝐺 𝑦 𝑖,𝑗
2
𝐼 𝑀 = 1 𝑖𝑓 𝛻 𝐼 ≥ 𝑡
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝐺 𝑥 = 𝑆 𝑋 ∗ 𝐼
𝐺 𝑦 = 𝑆 𝑦 ∗ 𝐼
𝐼 =
I − min(𝐼)
max 𝐼 − min(𝐼)
9. Traditional Segmentation
Problems
Assumes background only contains random noise
Foreground – structure
Background – no structure
Latent prints typically have structured
backgrounds
Resulting in many spurious minutiae when applying
traditional AFIS feature extraction methods
Also have similar structured background noise in the
fingerprint region itself
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10. Segmentation Method
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Input Fingerprint
Image
Input Fingerprint
Image
Normalize Image
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Generate ideal ridge
template
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Generate ideal ridge
template
Adjust template to
image mean and
variance
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Generate ideal ridge
template
Adjust template to
image mean and
variance
Take cross sectional
slice orthogonal to
ridge flow at anchor
point within
foreground region
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Cross-correlation of
cross sectional
region with ideal
template
Generate ideal ridge
template
Adjust template to
image mean and
variance
Take cross sectional
slice orthogonal to
ridge flow at anchor
point within
foreground region
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Cross-correlation of
cross sectional
region with ideal
template
Generate ideal ridge
template
Adjust template to
image mean and
variance
Threshold goodness
of fit score to
determine
foreground region
(quality levels) and
background region
Take cross sectional
slice orthogonal to
ridge flow at anchor
point within
foreground region
Find local ridge
frequency map
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Cross-correlation of
cross sectional
region with ideal
template
Generate ideal ridge
template
Adjust template to
image mean and
variance
Threshold goodness
of fit score to
determine
foreground region
(quality levels) and
background region
Segmented
Fingerprint Image
Take cross sectional
slice orthogonal to
ridge flow at anchor
point within
foreground region
Find local ridge
frequency map
Repeatforallblocksinfingerprintregion
Generate Ideal
Template
Input Fingerprint
Image
Normalize Image
Threshold
normalized
intensities to find
initial foreground
region
Cross-correlation of
cross sectional
region with ideal
template
Generate ideal ridge
template
Adjust template to
image mean and
variance
Threshold goodness
of fit score to
determine
foreground region
(quality levels) and
background region
Segmented
Fingerprint Image
Take cross sectional
slice orthogonal to
ridge flow at anchor
point within
foreground region
Find local ridge
frequency map
11. Ridge Template Generation
“Ideal” Ridge Template
Modeled by
𝑇𝑖 = sin 2𝜋𝑓𝑑𝑖 −
𝜋
2
= −cos 2𝜋𝑓𝑑𝑖, , ∀𝑗
Adjust normalized template to mean and variance of image by
𝑇𝑖 = 𝜎𝑖𝑚𝑔 ∙ 𝑇𝑖 + 𝜇𝑖𝑚𝑔, ∀𝑗
𝑓 𝑑13 = 3
Observed 𝑥-signature Ideal 𝑥-signature
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13. Segmentation Results (cont.)
Fingerprint Area
(% of total
Image)
False Negatives (% of true
minutiae labelled as
background)
NBIS 60.7 1.41
P1 60.7 0.29
P2 33.6 1.47
P3 45.2 0.69
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14. Line Detection
Latent fingerprint matching (Jain and Feng)
Ridge flow direction
Negative cost associated with ridge directions that do not match
lines which dominate the local ridge flow direction, decrease
match score
Detect lines and remove from directional flow computation
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15. Line Detection
Hough-based approach
A line passing through a point (𝑥, 𝑦), 𝑦 = 𝑚𝑥 + 𝑏 is represented in
Hough space as
𝑟 = 𝑥𝑐𝑜𝑠(𝜃) + 𝑦𝑠𝑖𝑛(𝜃)
Collinear spatial points are represented by intersecting curves in
Hough space
Accumulator is used to find highest frequency parameters, (𝑟, 𝜃),
corresponding to points occurring in image
1/29/2015
17. Future Work
1/29/2015
Use classifier to determine
background/foreground and quality, instead of
threshold
Adjust template for ridge thickness
Performance results with refined directional
map
Detect and remove errors caused by text in
background
Accidental friction ridge skin impression left on a surface (crime scene)
Typically not visible, made visible by chemicals like powders ninhydrin then photographed or lifted with adhesive