Abstract
This paper considers the problem of uniquely identifying an individual based on a palm print. We survey 100 palm prints, tallying the number and locations of 12 friction ridge characteristics (minutiae) on each palm with the assistance of an automated print identification system (AFIS) called AFIX Tracker. After binning the data set by frequency ranges, the individual entropy values are calculated for every minutia in each section of the palm. The minimum entropies for each minutia/section combination are summed to provide a probability of misidentification based on a full palm print. Our results show that the probability of misidentification is on the order of 10^(-29). This mirrors previous reports in the literature that estimated 10^(-20) for fingerprints and even improves upon it.
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