This is a fingerprint pattern and ridge count analysis for two population groups used to associate an individual to a group through qualitative and quantitative comparison. The fingerprint data from the two groups were analyzed using a Classification and Regression Tree algorithm. Four distinct trees were produced. The first tree separated the two populations using only finger number and pattern. Subsequent trees separated the two populations using finger number, pattern, and ridge count. Including ridge counts increased the per-finger classification accuracy from 56.4% to 73.9% and 79.5% for right and left loop patterns respectively. Whorls with both ridge counts improved the classification accuracy to 83.3%. The classification accuracies provided the basis for determining the probability of correctly associating a person to one of the two groups. For each finger, the probability of correctly associating the finger to the group is binomially distributed based upon the classification probabilities. Association is based upon a majority vote. In the worst case with only finger pattern and finger number available, the expected probability of correctly associating the individual is 54.1% using all ten fingers. Adding ridge counts raises the lower bound to 90.8%. The upper bound using whorls with two ridge counts is 98.4%. Between these two extremes are cases in which the patterns vary among the fingers. Because the probability of correctly associating the individual to the city depends on the data available, cases where the fingerprint patterns or the deltas are not discernible reduce the probability of correct association accordingly.
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