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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3. 1/29/2015
*Images from NIST SD27
Latent vs. Plain/Rolled Minutia
Count*

4. Latent vs. Plain/Rolled Minutia
Count
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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
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7. Fingerprint Identification
 Segment Fingerprint
Image
 Enhance Fingerprint
Ridges
 Find Binary Image
 Find Ridge Skeleton
 Extract Minutiae
 Match Sample
template with
database
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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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12. Segmentation Results
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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
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16. Line Detection Results
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17. Future Work
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 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

18. Thank you!
 Questions?