基于对指纹数据的卷积神经网络分析的性别推断
Yibo Zhang1, Shengfeng Cui2, Jiaqi Li2
1School of Forensic and Technology, Zhengzhou Police University, Zhengzhou City, Henan Province, China. 601887522@qq.com.
Scientific reports
|December 1, 2025
概括
这项研究引入了一个卷积神经网络 (CNN) 来从指纹中确定生物性别. 人工智能模型实现了高精度,为法医科学应用提供了一个新的工具.
科学领域:
- 法医科学 法医科学 法医科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 从指纹中确定生物性别是一个新兴的法医科学挑战.
- 通过指纹确定性别的传统方法存在局限性.
- 卷积神经网络 (CNN) 提供了一个潜在的自动化方法.
研究的目的:
- 开发和验证一个轻量级的CNN用于从指纹特征推断生物性别.
- 评估CNN在独立数据集上的准确性和概括能力.
- 通过使用类激活映射 (CAM) 提高CNN模型的可解释性.
主要方法:
- 来自200名志愿者的1000张指纹图像的数据集被策划.
- 一个双卷积层CNN被设计和优化.
- 该模型使用独立测试集和五倍交叉验证进行了评估.
- 类激活映射 (CAM) 用于可视化模型焦点.
主要成果:
- 美国有线电视新闻网 (CNN) 的验证准确率为91.00%,测试准确率为95.00%.
- 曲线下的面积 (AUC) 值达到0.974 (验证) 和0.983 (测试).
- 五次交叉验证证实稳定的性能,平均准确率为90.60% (SD:2.04%).
结论:
- 开发的CNN是一种高效可靠的工具,可以从指纹中推断出生物性别.
- 该模型表现出与传统法医方法相比的或优于传统法医方法的性能.
- 美国有线电视新闻网 (CNN) 显示出作为法医识别中的补充工具的潜力,通过CAM提高了可解释性.
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