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基于小波变换和傅里叶变换的颅骨性别估计

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform.

机构信息

College of Information Science and Technology, Northwest University, Xi'an, China.

College of Information Science and Technology, Beijing Normal University, Beijing, China.

出版信息

Biomed Res Int. 2020 Jan 11;2020:8608209. doi: 10.1155/2020/8608209. eCollection 2020.

Abstract

Skull sex estimation is one of the hot research topics in forensic anthropology, and has important research value in the fields of criminal investigation, archeology, anthropology, and so on. Sex estimation of skull is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study is to establish a skull-based sex estimation model in Chinese population, providing a scientific reference for the practical application of forensic medicine and anthropology. We take the superior orbital margin and frontal bone of the skull as the research object and proposed a technology of objective sex estimation of the skull using wavelet transform and Fourier transform. Firstly, the supraorbital margin and frontal bone were quantified by wavelet transform and Fourier transform, and then the extracted features were classified by SVM, and the model was tested. The experimental results show that the accuracy rate of male and female sex discrimination is 90.9% and 94.4%, respectively, which is higher than that of morphological and measurement methods. Compared with the traditional methods, the method has more theoretical basis and objectivity, and the correct rate is higher.

摘要

颅骨性别鉴定是法医人类学中的热门研究课题之一,在刑事侦查、考古学、人类学等领域具有重要的研究价值。颅骨性别鉴定在法医学和人类学的实际应用中具有重要意义,无论是在涉及活体的法律情况下,还是在识别尸体方面。本研究旨在建立中国人颅骨性别鉴定模型,为法医学和人类学的实际应用提供科学参考。我们以眶上缘和额骨为研究对象,提出了一种基于颅骨的小波变换和傅里叶变换的客观性别鉴定技术。首先,通过小波变换和傅里叶变换对眶上缘和额骨进行量化,然后通过 SVM 对提取的特征进行分类,并对模型进行测试。实验结果表明,男性和女性性别识别的准确率分别为 90.9%和 94.4%,高于形态学和测量方法。与传统方法相比,该方法具有更坚实的理论基础和客观性,准确率更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd6a/7201841/5d9ad95362b5/BMRI2020-8608209.001.jpg

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