Details

Autor(en) / Beteiligte

• Yang, Wen
• Liu, Xiaoning
• Zhu, Fei
• Geng, Guohua
• Li, Kang

Titel

Determination of Sex Discriminant Function Analysis in Chinese Human Skulls

Ist Teil von

• Biometric Recognition, p.589-598

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Introduction: Human identity and sex determination are crucial for forensic investigations. The human skull is a useful tool for identities in natural disasters and criminal investigations. Aim: Using stepwise Fisher and logistic regression to build multivariate linear discriminant function to achieve sex determination for Uighur adult skull of Turpan, Xinjiang. Methods: Using CT equipment to acquire and reconstruct 267 (114 males and 153 females) three-dimensional skull models. Sixteen measurement indicators were measured and computed. Stepwise Fisher and logistic regression was performed to build the sex discriminant function and leave-one-out cross validation was used to evaluate accuracy. Results: Average of fifteen measurement indicators of male was bigger than that of female. Only one measurement indicator of male was smaller than female. Except two indicators (X7 and X13), the other existed significant difference (p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ p $$\end{document} < 0.01). According to sex discriminant function consisting of four indicators (X1, X4, X10, X11), using stepwise Fisher method, the accuracy of male was 86.8% and female was 86.2%. According to sex discriminant function consisting of five indicators (X4, X6, X12, X15, X16), using Logistic method, the accuracy of male was 89.4% and female was 90.2%. According to sex discriminant function consisting of incomplete skull with only frontal and mandibular, using stepwise Fisher method, the accuracy of male was 67.9% and female was 69.1%. Using Logistic method, the accuracy of male was 68.7% and female was 70.4%. Conclusion: By combining computer software with machine learning classification algorithm, the sex discrimination of complete skull and incomplete skull can be realized. In the gender identification of the Uygur population, the Logistic regression method is better than the stepwise Fisher method.