Statistics of fingerprint minutiae frequency and distribution based on automatic minutiae detection method

Abstract

The Daubert case in Philadelphia in 1999 caused a debate about the scientificity of fingerprint evidence. Since then, the current fingerprint identification system has been constantly challenged and questioned. Quantitative identification technology based on the statistics of fingerprint minutiae has become a new research hot spot. In this paper, an automatic detection algorithm is designed to achieve automatic classification of fingerprint minutiae using the deep convolution neural network YOLOv5 model. Then the occurrence frequencies of minutiae are statistically evaluated in 619,297 fingerprint images. The results show that the frequency ranges (unit%) of six types of minutiae per finger are ridge endings [68.49, 70.81], bifurcations [26.37, 27.26], independent ridges [1.533, 1.626], spurs [1.129, 1.198], lakes [0.4588, 0.4963], crossovers [0.3034, 0.3256]. The results also show that there are differences in the distribution frequency of the six types of minutiae in the ten finger positions ( thumb, middle, ring, index and little finger of the left and right hand) and in the four finger patterns ( arch, left loop, right loop and whorl). From the quantitative point of view of fingerprint identification, this paper calculates the number and frequency ranges of six types of minutiae, distinguishes the evaluation value of each type of minutiae, and provides the basic data support for establishing a probability model of fingerprint identification in the future.

Keywords: Fingerprint identification; Fingerprint minutiae recognition; Fingerprint minutiae statistics; Target detection.