人类学学报 ›› 2022, Vol. 41 ›› Issue (06): 1047-1057.doi: 10.16359/j.1000-3193/AAS.2021.0088

• 研究论文 • 上一篇    下一篇

指纹与年龄相关性的量化分析

宋焕庭(), 唐玮, 张丽梅, 张忠良(), 张嘉宇, 陈世韬   

  1. 中国刑事警察学院刑事科学技术学院,沈阳 110035
  • 收稿日期:2021-06-28 修回日期:2021-10-21 出版日期:2022-12-15 发布日期:2022-12-19
  • 通讯作者: 张忠良
  • 作者简介:宋焕庭,研究生,主要从事皮纹学研究。E-mail: s15535002374@163.com
  • 基金资助:
    公安部重点研究计划项目(2011ZDYJXJXY001)

Quantitative analysis of the correlation between fingerprint and age

SONG Huanting(), TANG Wei, ZHANG Limei, ZHANG Zhongliang(), ZHANG Jiayu, CHEN Shitao   

  1. Department of Criminal Science and Technology, Criminal Investigation Police University of China, Shenyang 110035
  • Received:2021-06-28 Revised:2021-10-21 Online:2022-12-15 Published:2022-12-19
  • Contact: ZHANG Zhongliang

摘要:

本文利用Leica M125体视显微镜对1510份样本中右手拇指的皱纹、乳突纹线密度、乳突纹线和小犁沟的宽度、细点线、屈肌褶纹等特征进行测量,通过SPSS软件分析年龄信息特征的相关性,选取其中相关性大的变量进行多元线性回归,并得出推断公式以分析并量化随年龄的增长指纹与年龄相关信息的变化特点及规律。研究结果显示,手印中的皱纹、乳突纹线密度、乳突纹线和小犁沟的宽度、细点线、屈肌褶纹等特征与年龄具有相关性,但利用这些变量构建的多元回归模型拟合优度并不高;皱纹、乳突纹线密度、乳突纹线和小犁沟的宽度、细点线、屈肌褶纹等可作为手印分析年龄的参考和辅助特征。乳突纹线的边缘形态、手印印痕的模糊程度、汗孔等以当前科技手段难以用测量描述特征的年龄信息,在手印分析年龄中或可发挥核心价值,具体还需进一步探究。

关键词: 指纹, 年龄, 生物人类学, 皮纹学, 统计学

Abstract:

A difficult problem in the study of criminology is the determination of age and other personal characteristics of criminal suspects, especially the use of fingerprints. For a long time, many scholars noted subtle changes in fingerprints with an increase in age, but presently there is a lack of specific correlation between the characteristics of fingerprints that change with age and the age of the individual. In this work, we use the Leica M125 volumetric microscope to measure right thumb wrinkles, density of friction ridges, width of friction ridges and small furrows, fine point lines and flexion crease in 1510 samples. The correlation between feature information in the right thumb and age was analyzed with SPSS software. Variables showing high correlation were selected for multiple linear regression. We found features of wrinkles, friction ridges density, width of friction ridges and small furrows, fine point lines and flexor crease in fingerprints all correlated with age, but the goodness of fit of the multiple regression model based on these variables was not high. Then we used principal component analysis to recombine the variables and conducted multiple regression analysis again. An ideal model was still not obtained, and we believe this model cannot be applied to the practice for accurate fingerprint age analysis. For research on fingerprints and age, these anatomical features listed above can be used as for reference. Age information that is difficult to measure and express (characteristics such as edge shape of friction ridges, degree of ambiguity of fingerprint marks, and perspiration holes) may play a key role in fingerprints age analysis, therefore such researches need to be continued.

Key words: fingerprint, age, biological anthropology, dermatoglyphics, statistics

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