Analytical Services
Optics & Image Processing
When fingerprints are classified, analysts use both the overall loop pattern of the skin’s ridges and “minutiae”, the specific locations where the ridges either split in two or end. With enough computing power, the minutiae alone can be very useful for automatically matching a scanned image of an individual’s fingerprint against a database of prints. This is the basis of AFIS, the FBI’s well known identification system.
But there are problems, as a government customer explained: When the finger receives a cut, this produces a large number of new “endings”, minutiae that cannot match any previous print — and may not even match future ones, if the cut heals without scarring. Creases do the same. This is especially true in heavy manual labor (such as housecleaning!), which produces so many breaks that the sheer number of apparent minutiae might match practically anything.
Also, if there are any stray marks on the image, stray materials on the finger, or fabric patterns underlying the print, these can falsely introduce or eliminate large numbers of minutiae.
We
noted, however, that all the new endings
occur in matched pairs, and all of the other defects introduce patterns
that do not resemble the strong patterns of fingerprint ridges. This
allowed us to devise algorithms to remove the new endings from the
images, “rejoining” the cut ridges and suppressing the defect patterns.
The result is an image of the print in a more pristine form: with just
the ridges of the finger, splitting or ending just where they
originally did on the finger. No cuts, no marks, no materials, no
fabric.
Upper left:
The original fingerprint image
shows the problems:
- - a smudged area with poorly defined ridges near the
top,
from dirt and oil on the fingertip;
- - a dark region of very little detail near the center;
- - a large crease on the left, just below the center, as well as other creases.
Upper right:
For each small section of the image, the processing identifies both the
dominant local direction of ridge-like features, and the degree of
contrast (variance) between the ridges and valleys.
Lower left:
The first cleanup: The process discards all details that are either too
fine or too coarse to be ridges, regardless of direction. Then it
identifies which pixels (picture cells) are brighter or darker than the
local average by an amount equal to the contrast shown in the upper
right image. Other pixels are assigned an intermediate shade of grey.
(Despite the label, the image is not binary, i.e., not only black and
white.)
Lower right:
The second cleanup: Features that do not agree with the local direction
(upper right image) -- for example, creases, cuts, and even handwriting
(not shown) -- are now discarded. Most of the creases here are removed,
with only faint ripples to show where they used to be.
The focus of processing is on clarifying the overall structure: a loop in the case, with its center slightly below and to the left of the image's center. However, the result also yields well determined minutiae, places where a ridge ends or splits, over a much larger part of the fingertip, with very few false "ends" where a crease separates each ridge into two pieces. The processing here occurs independently in each small region, so even a very partial fingerprint can be processed, and minutiae revealed.
For more information on this project or any other services available from STAR Analytical Services, please contact us by email at info@STARAnalyticalServices.com or phone 781-861-STAR (7827).
