Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons
Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons
复制标题
DOI：
10.1111/1556-4029.14914
复制
发表时间：
2021-10-11
影响因子：
1.6
通讯作者：
Caligiuri, Michael P.
中科院分区：
文献类型：
--
作者：
Fuglsby, Cami;Saunders, Christopher;Caligiuri, Michael P.
复杂的自动手写识别系统的最新进展已经导致这些系统的计算过程和特征缺乏可理解性，这些系统被设计为支持法医手写检查者。为了缓解这个问题，本研究研究了两个系统之间的关系：FLASH ID（R），一个自动手写/黑盒系统，使用从手写静态图像中提取的测量值，和MovAlyzeR（R），一个从笔画中捕获运动学特征的系统。在这项研究中，33位作家每人用草书和手写体写下了伦敦信中的60个短语，这导致了数千个样本对的分析。使用两个评分函数（每个系统一个）计算样本对之间的差异。观察到的结果表明，基于运动学空间几何笔画特征的相异性分数（例如，幅度和倾斜度）与使用FLASH ID系统所使用的静态的、基于图形的特征获得的相异性分数具有统计上显著的关系。对于时间特征观察到类似的关系（例如，持续时间和速度），但不是笔的压力，并为手写和草书样本。这些结果强烈地暗示，在测量成对手写样本的差异时，FLASH ID（R）和MovAlyzeR（R）的当前实现都依赖于相似的特征集。这些结果表明，使用MovAlyzeR（R）的生物识别研究，特别是那些基于空间几何特征集的研究，支持基于FLASH ID（R）输出的生物识别匹配算法的有效性。
Recent advances in complex automated handwriting identification systems have led to a lack of understandability of these systems' computational processes and features by the forensic handwriting examiners that they are designed to support. To mitigate this issue, this research studied the relationship between two systems: FLASH ID(R), an automated handwriting/black box system that uses measurements extracted from a static image of handwriting, and MovAlyzeR(R), a system that captures kinematic features from pen strokes. For this study, 33 writers each wrote 60 phrases from the London Letter using cursive writing and handprinting, which led to thousands of sample pairs for analysis. The dissimilarities between pairs of samples were calculated using two score functions (one for each system). The observed results indicate that dissimilarity scores based on kinematic spatial-geometric pen stroke features (e.g., amplitude and slant) have a statistically significant relationship with dissimilarity scores obtained using static, graph-based features used by the FLASH ID(R) system. Similar relationships were observed for temporal features (e.g., duration and velocity) but not pen pressure, and for both handprinting and cursive samples. These results strongly imply that both the current implementation of FLASH ID(R) and MovAlyzeR(R) rely on similar features sets when measuring differences in pairs of handwritten samples. These results suggest that studies of biometric discrimination using MovAlyzeR(R), specifically those based on the spatial-geometric feature set, support the validity of biometric matching algorithms based on FLASH ID(R) output.