Fingerprint evidence can benefit in image quality if transformed using digital image processing
techniques. This is especially true when considering prints that cannot be easily lifted (such as those deposited on porous paper substrates), or when the mechanism of lifting does not effectively reduce
background interferences. In these instances, frequency filtering is one type of mathematical
transformation that can serve to increase image clarity and the ability to extract minutiae relevant
to pairwise comparisons. To quantify the impact of frequency filtering on image quality, high quality and
low quality (noisy) prints were collected. The high quality prints served as exemplars that were
compared to the low quality prints both pre- and post-filtering. The resulting pairwise match scores
indicate that: (1) frequency filtering has a low probability of creating false positive associations, (2) 90%
of the post-filtered images result in a normalized gain in match score, (3) frequency filtering doubled the
probability of obtaining match scores greater than 30% (for the automated algorithm employed in this
study), and (4) filtering can double the probability of obtaining 10 or more matching minutiae when
comparing same source prints. Overall, the research indicates a reasonable and quantifiable payoff in
increased clarity, matchingminutiae and pairwise similarity for post-filtered images when compared to
known-match exemplars.
Fingerprint evidence can benefit in image quality if transformed using digital image processingtechniques. This is especially true when considering prints that cannot be easily lifted (such as those deposited on porous paper substrates), or when the mechanism of lifting does not effectively reducebackground interferences. In these instances, frequency filtering is one type of mathematicaltransformation that can serve to increase image clarity and the ability to extract minutiae relevantto pairwise comparisons. To quantify the impact of frequency filtering on image quality, high quality andlow quality (noisy) prints were collected. The high quality prints served as exemplars that werecompared to the low quality prints both pre- and post-filtering. The resulting pairwise match scoresindicate that: (1) frequency filtering has a low probability of creating false positive associations, (2) 90%of the post-filtered images result in a normalized gain in match score, (3) frequency filtering doubled theprobability of obtaining match scores greater than 30% (for the automated algorithm employed in thisstudy), and (4) filtering can double the probability of obtaining 10 or more matching minutiae whencomparing same source prints. Overall, the research indicates a reasonable and quantifiable payoff inincreased clarity, matchingminutiae and pairwise similarity for post-filtered images when compared toknown-match exemplars.
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