Advances in Fingerprint Technology.pdf

that, for the experiments as designed, the two populations are too close to
distinguish.
In actuality, we do not know how many pairs of different prints from
the same finger are present among the 50,000. Three pairs were uncovered,
but only because of the unusually high Z scores. There could well be more.
Compounding and including this most fundamental error, the second
experiment also employed the thoroughly discredited practice of using subsets
of inked prints to simulate latent prints. Sparrow 66 has also commented
on this conceptual error:
“A much more common error is to create artificial latent marks by masking
rolled images. Occasionally, the images are deliberately blurred (smudged
or de-focused), and some areas are obscured. Then the newly created partial
prints are entered as latent, with the original rolled images being incorporated
into the file database. This approach fails to emulate the real world
of operational fingerprint comparison in that no distortion of any kind is
introduced ⎯ neither spatial nor topological. The search print is actually
a piece of the matching file print image. Again, this tests the system’s ability
to match pieces of identical images, not different images made by the same
finger.”
It is remarkable that a study with such fundamental flaws was presented
in court, especially with the internal inconsistencies of the “same finger/different
print” comparisons. The defects were immediately recognized and fully
explained in opposing briefs and testimony. Subsequently, Wayman provided
a criticism, “When Bad Science Leads to Good Law.” 65 In addition to pointing
out the fundamental errors of comparing the images to themselves and the
use of masked pseudo-latents, Wayman questioned the assumption of normality
in the data.
“The comparison of images to themselves lead, of course, to extremely high
scores, which researchers called the ‘perfect match’ score. Because in life
fingerprints are always changing, no real comparison of two different images
of the same finger will ever yield such a high score. By adopting, as definition
of ‘in common,’ the score obtained by identical images, the government very
strongly biased any results in the government’s favor.
“Now the government did something worse: They looked at all the
scores between different fingerprint images and declared them to follow a
‘bell curve.’ There are potentially an infinite number of curves that could
fit the data, some better than others. There are simple tests available to show
if the ‘bell curve,’ or any other curve, roughly fits the data. No such tests,
which might have eliminated the ‘bell curve’ assumption, were performed,
however. Now, the government simply pulled out a college-level textbook
on statistical estimation and, based on the ‘bell curve’ assumption, found