通过机器学习算法对性别分类的准确性 - - 人类耳朵和鼻子的形态变量

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Tej Kaur¹, Kewal Krishan², Akanksha Sharma¹

¹Institute of Forensic Science and Criminology, Panjab University, Sector-14, Chandigarh, India.

BMC research notes

|April 15, 2025

概括

这项研究使用机器学习从耳朵和鼻子测量中预测性别,达到86.75%的准确性. 鼻子宽度是法医科学中个人识别最重要的预测因素.

科学领域:

法医人类学法医人类学.
生物识别信息生物识别信息
机器学习应用程序机器学习应用程序

背景情况:

准确的性别确定对于法医和医疗法律环境中的个人身份识别至关重要.
面部特征,特别是耳朵和鼻子形态,为性别估计提供了潜在的生物识别标记.
传统方法可能有局限性,需要新的方法.

研究的目的:

开发和评估一个机器学习模型,使用耳朵和鼻子的测量来准确预测性别.
确定最重要的面部参数,有助于确定性别.
评估PyCaret库在法医性别分类中的有效性.

主要方法:

使用了508名参与者 (北印度,18-35岁) 的数据集,记录了耳朵和鼻子的测量.
使用PyCaret机器学习库,实现了一种训练-评估-测试验证方法.
在基于准确性和计算时间的多个模型进行比较后,物流回归被确定为表现最好的分类器.

主要成果:

后勤回归模型实现了86.75%的性别预测准确度.
鼻子宽度被确定为准确的性别预测最重要的变量.
大多数耳朵和鼻子测量表明,对性变态有显著的贡献.

结论:

机器学习,特别是使用PyCaret,提供了一种高效的方法来确定从面人类学的性别.
鼻子宽度是法医调查中性别估计的关键生物识别指标.
这种方法可以在法医检查和犯罪现场调查中增强个人身份识别.

关键词:

耳鼻形态耳鼻形态耳鼻形态法医识别识别法医识别人类解剖学和形态学人类解剖学和形态学机器学习算法机器学习算法这是PyCaret的PyCaret.性别分类的性别分类.

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