在不同的设备和条件下关于手写稳定性的研究
Hsiang-Ju Lai1, Long-Huang Tsai2, Kung-Yang Hsu3
1Forensic Science Division, Ministry of Justice Investigation Bureau, New Taipei City 231209, Taiwan.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
数字签名捕获有价值的数据,如压力和速度,帮助法医分析. 笔的使用提高了区分真伪数字签名的准确性,提供了新的法医见解.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
背景情况:
- 从传统手写签名向数字手写签名的转变给法医学带来了挑战.
- 数字捕获签名 (DCS) 提供了超出视觉外观的丰富数据,包括压力,速度和冲动顺序.
- 现有的法医方法需要适应数字签名分析.
研究的目的:
- 评估数字捕获签名 (DCS) 的法医潜力.
- 评估时间序列对齐算法的有效性,用于分析DCS数据.
- 区分同一作者和不同作者的数字签名.
主要方法:
- 使用了三星Galaxy Tab S10,果iPad Pro和iPad Mini带着笔.
- 开发了定制的手写捕获软件,适用于Android和iOS.
- 应用FastDTW和SC-DTW算法来分析签名数据 (坐标,压力).
- 在数据预处理中使用字符中心点对齐.
主要成果:
- 通过字符中心点对齐进行预处理,显著减少了平均积累的手写距离.
- DCS分析有效量化了手写的稳定性,并将相同的样本与不同的样本区分开来.
- 基于笔的数字签名比基于手指的数字签名更准确地产生了作家的区别.
结论:
- 数字捕获签名为签名验证提供了有价值的法医数据.
- 时间序列对齐算法与预处理相结合是分析DCS的有效方法.
- 笔输入增强了数字签名法医学的辨别能力.
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