The focal point of our project is to compare the differences between dirty fingerprints and cropped fingerprints using the data we have collected. During the procedure we captured three clean and three dirty fingerprints from each person in the group. We then analyzed the score, minutiae points, core/delta, good and poor quality of each of the fingerprints. The importance of this project is to analyze the impact of dirty fingerprints on quality.

1. THE IMPACT OF DIRTY FINGERPRINTS
The focal point of our project is to compare the differences between dirty fingerprints and cropped fingerprints using the data we have
collected. During the procedure we captured three clean and three dirty fingerprints from each person in the group. We then analyzed the
score, minutiae points, core/delta, good and poor quality of each of the fingerprints. The importance of this project is to analyze the
impact of dirty fingerprints on quality.
Connor Dawes, Clark Ford, Ben Petry, Michael Brockly, Stephen Elliott
Overview
Subject 1
Clean Dirty
Subject 2
Clean Dirty
Subject 3
Clean Dirty
Cropped Fingerprints
• Cropped fingerprints
rely on taking an
already whole print and
cutting a certain portion
or percentage out
• Scanner technology is
not a factor
• Past research has been
done to ID artificial poor
quality prints, but only
by cropping the image
post-collection
• The purpose of
cropping is to see what
percent of the
fingerprint can be
missing and the system
still make a positive ID
Dirty Fingerprints
• Many variables that
may impact the
scanners’ ability to
function (dryness, grit,
grade of soil, etc.)
• A large factor is the
scanners’ limitations of
collecting viable data
points from a low quality
fingerprint
• More realistic effect to
real world operating
conditions
• No formal research was
found on this topic,
possibly due to the
difficulty in quantifying
“dirtiness”
• A dirty fingerprint has
random data destruction
VS. VS.
GATHERED DATA
Clean Fingerprints Dirty Fingerprints
Average Minutiae Points 60.66 55.78
Average Good Score 450.67 371.89
Average Poor Score 136.89 225.78
Average Core/Delta ID 1.33 0.33
The above data represents a test of three individuals.
The quantity of poor quality pixels from
scans of dirty prints was much higher as
indicated from the above graph.
The number of usable minutiae points tended to
not follow an exact model. We believe this is due
to foreign material creating false minutiae points.
Minutiae Points = The number of identified minutiae points in the image that can be used
Good Score = The number of usable pixels in the image
Poor Score = The number of unusable pixels in the image
Core/Delta ID = The number of identified cores and deltas in the image
Below is an example of
matching a cropped portion
of an image to the original
Below is an example of
matching a randomly altered
(dirty) image to the original
Source: http://www.aware.com/biometrics/qualitycheck.html