Gao Mengting, Tang Yunqi, Liu Huan, Ma Rongliang
School of Criminal Investigation, People's Public Security University of China, Beijing 10038, China.
School of Criminal Investigation, People's Public Security University of China, Beijing 10038, China; Beijing Municipal Key Laboratory of Forensic Science, Beijing 10038, China.
Forensic Sci Int. 2023 Mar;344:111572. doi: 10.1016/j.forsciint.2023.111572. Epub 2023 Jan 23.
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.
1999年在费城发生的多伯特案引发了关于指纹证据科学性的争论。从那时起，当前的指纹识别系统不断受到挑战和质疑。基于指纹细节特征统计的定量识别技术成为新的研究热点。本文设计了一种自动检测算法，利用深度卷积神经网络YOLOv5模型实现指纹细节特征的自动分类。然后在619297幅指纹图像中对细节特征的出现频率进行统计评估。结果表明，每根手指上六种类型细节特征的频率范围（单位%）分别为：嵴终点[68.49, 70.81]、分叉点[26.37, 27.26]、独立嵴[1.533, 1.626]、短纹[1.129, 1.198]、湖状纹[0.4588, 0.4963]、交叉纹[0.3034, 0.3256]。结果还表明，六种类型的细节特征在十个手指位置（左手和右手的拇指、中指、环指、食指和小指）以及四种指纹图案（弓型纹、左旋箕、右旋箕和斗型纹）中的分布频率存在差异。从指纹识别的定量角度出发，本文计算了六种类型细节特征的数量和频率范围，区分了每种类型细节特征的评估值，并为未来建立指纹识别概率模型提供了基础数据支持。
