Distribution of epidermal ridge minutiae

Abstract

The distribution of epidermal ridge minutiae on the distal portion of male human thumbprints has been characterized. For each of 412 thumbprints, a centrally located focal minutia was chosen and neighboring minutiae were sampled. Minutiae were considered to be neighbors if there were no other minutiae appearing in the intervening region defined by the two minutia events and the ridge system. For each neighbor minutia, the total ridge distance between the focal and neighbor minutiae was measured. This distance is the total length of ridges appearing in the intervening region.The number of neighbor minutiae occurring about the focal minutia was found to be normally distributed with a mean of 6.42 (n = 412). The distribution of total ridge distances was not adequately described by the negative exponential distribution, but was well described by a gamma distribution with a shape parameter of 0.193 and a scale parameter of 5.91 mm. This gamma distribution reflects a local overdispersion of minutiae.This study is noteworthy as the first to describe the distribution of epidermal ridge minutiae within the ridge structure. The results support prior work based on quadrat sampling and eliminate two possible sources of error.A possible explanation for the overdispersed distribution lies in the growth stress model for minutia formation. Minutia formation may alleviate local growth stress, thereby removing the impetus for formation of additional minutiae in the immediately surrounding region.

An Analysis of Minutiae Neighborhood Probabilities

“…The study [8] also points out that minutiae tend to cluster in areas where the ridge directions change abruptly. This result explains two previously opposite findings [9], [10] on minutiae dependency due to scale difference. Clustering tendency [9] was also found in a 2D Markov process over a grid of 1-mm cells at 5OOdpi (about 2-pixel wide) with 13 minutiae states per cell, while overdispersion [10] was observed when considering nearest neighboring minutiae only.…”

“…This result explains two previously opposite findings [9], [10] on minutiae dependency due to scale difference. Clustering tendency [9] was also found in a 2D Markov process over a grid of 1-mm cells at 5OOdpi (about 2-pixel wide) with 13 minutiae states per cell, while overdispersion [10] was observed when considering nearest neighboring minutiae only. Such global clustering tendency (by ridge path) and local overdispersion (by randomness of minutiae) were also revealed in Ashbaugh's book [11]: "The friction ridge path is influenced by the volar pads; however, the location of friction ridge path endings and bifurcations is random.…”

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

“…As the average ridge period was recorded to be 0.463 mm in Stoney (1988), for a fingerprint image with dots per inch (dpi) equal to R, the previous formula for the configuration can be expressed as K l = ⌈ 172.r l R ⌉. However, this was really only used as a rough guide for the configuration used in the experimentation of Tico & Kuosmanen (2003).…”

Fingerprint Identification: Potential Sources of Error and the Cause of Wrongful Convictions

“…Efforts to calculate the likelihood of uniqueness based on statistical models must be very careful to avoid unrealistic assumptions made in order to fit a formula, and non-representative or non-random sample populations (Page et al, 2011). The distribution model used in studies analysing population data has also been marked as a problem in past studies as well: the commonly held assumption that there is an equal probability for any individual to have a particular trait was disproved for fingerprints in the late 1980s (Stoney, 1988). The '50-K fingerprint study', as it is commonly referred to, also investigated uniqueness in prints for the same case mentioned above, United States v. Mitchell: the study compared 10,000 fingerprints of the same type of finger and the same basic ridge pattern in order to assess uniqueness of prints, but was criticized for making faulty assumptions about the distribution of their data when creating statistical models (Page et al, 2011).…”