Since recent advances in complex automated handwriting identification systems have led to a lack of understanding of these systems’ computational processes and features by the forensic handwriting examiners that they are designed to support, the current study mitigated this issue by examining the relationship between two systems: FLASH ID®, an automated handwriting/black box system that uses measurements extracted from a static image of handwriting, and MovAlyzeR®, a system that captures kinematic features from pen strokes.
In this study, 33 writers each wrote 60 phrases from the London Letter using cursive writing and hand printing, 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® system. Similar relationships were observed for temporal features (e.g., duration and velocity) but not pen pressure, and for both hand printing and cursive samples. These results strongly imply that both the current implementation of FLASH ID® and MovAlyzeR® rely on similar features sets when measuring differences in pairs of handwritten samples. These results suggest that studies of biometric discrimination using MovAlyzeR®, specifically those based on the spatial-geometric feature set, support the validity of biometric matching algorithms based on FLASH ID® output. (publisher abstract modified)