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基于深度学习的CNN模型用于指纹模式的多类分类
Apurav Mahajan1, Damini Siwan2, Peehul Krishan3
1Department of Anthropology, Panjab University, Chandigarh, India.
Medicine, science, and the law
|July 4, 2025
概括
这项研究介绍了一种用于分类指纹的人工智能模型. 卷积神经网络 (CNN) 实现了高精度,有助于在法医应用中更快地进行指纹分析.
科学领域:
- 生物识别信息 生物识别信息
- 人工智能的人工智能
- 法医科学 法医科学 法医科学
背景情况:
- 指纹是全球用于识别和安全的独特生物识别标识符.
- 手动指纹分类是耗时的,需要专业知识.
- 自动化系统可以提高指纹匹配和犯罪现场分析的效率.
研究的目的:
- 开发和评估用于多类指纹模式分类的卷积神经网络 (CNN) 模型.
- 将指纹分类为亨利的类别:弧形,环形,旋形和复合材料.
- 评估CNN模型在协助法医检查方面的表现.
主要方法:
- 一个卷积神经网络 (CNN) 模型被设计用于多类指纹分类.
- 该模型是在200名参与者的2000个指纹模式的数据集上进行训练的.
- 数据集被分为培训,测试和验证集 (8:1:1比).
主要成果:
- 在CNN模型中,训练准确率达到89%,验证准确率达到84%,测试准确率达到85.5%.
- 用测试数据集的混矩阵来评估性能.
- 该模型在四个主要的指纹模式中展示了有效的分类.
结论:
- 开发的CNN模型为指纹分类提供了可靠的自动化工具.
- 这种人工智能驱动的方法可以显著提高法医调查中指纹分析的速度和准确性.
- 该模型通过提供快速分类来支持指纹研究和犯罪现场分析.
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