一个定制的CNN模型用于签名身份验证-法医学含义
Rakesh Meena1,2, Damini Siwan3, Ankita Guleria1
1Department of Anthropology, Panjab University, Chandigarh, India.
Medicine, science, and the law
|February 13, 2026
概括
这项研究开发了一种定制的深度学习模型用于签名认证,在区分真实和伪造签名方面实现了高精度. 该模型对现实世界中的法医和银行应用非常有希望.
科学领域:
- 计算机科学 计算机科学
- 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:
- 签名认证对于验证身份和防止欺诈至关重要.
- 传统的方法可能是耗时和主观的.
- 深度学习为自动化和准确的签名验证提供了潜力.
研究的目的:
- 为签名认证定制基于深度学习的卷积神经网络 (CNN) 模型.
- 在真实和伪造签名的数据集上评估模型的性能.
主要方法:
- 一个卷积神经网络 (CNN) 模型被定制并训练在1400个签名图像上 (700个是真实的,700个是伪造的).
- 数据集被分为培训 (1000个样本) 和测试 (400个样本) 集.
- 使用超参数调整优化了模型架构.
主要成果:
- 该模型实现了高准确率:97.32% (培训),97.92% (验证) 和84.5% (测试).
- 其他性能指标包括精度 (85%),回忆 (84%),F1得分 (84%) 和特异性 (90%).
- 与现有方法相比,拟议的模型表现出优越的性能.
结论:
- 定制的CNN架构为签名认证提供了有效的解决方案.
- 该模型可以在更大的数据集上进行进一步训练,以提高性能.
- 潜在的应用包括法医文档检查,银行业和法律环境.
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