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.
摘要：
1999年费城的Daubert案引起了关于指纹证据科学性的争论。从那以后,当前的指纹识别系统不断受到挑战和质疑。基于指纹细节统计的定量识别技术已成为新的研究热点。在本文中,利用深度卷积神经网络YOLOv5模型,设计了一种自动检测算法,实现指纹特征点的自动分类。然后在619,297张指纹图像中统计地评估细节的出现频率。结果表明，每个手指的六种细节的频率范围（单位％）是脊端[68.49，70.81]，分叉[26.37,27.26],独立脊[1.533，1.626]，马刺[1.129，1.198]，湖泊[0.4588,0.4963],交叉[0.3034，0.3256]。结果还表明，在十个手指位置（拇指，中间,戒指,左手和右手的食指和小指)以及四个手指模式(足弓，左循环,右循环和螺纹)。从指纹识别的定量角度,本文计算了六种细节点的数量和频率范围，区分每种细节的评估值，为今后建立指纹识别概率模型提供了基础数据支持。
