Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise
Cami Fuglsby1, Christopher Saunders1, Danica M Ommen2
1Department of Mathematics and Statistics, South Dakota State University, Brookings, South Dakota, USA.
Journal of Forensic Sciences
|October 11, 2021
Summary
Forensic handwriting analysis systems like FLASH ID® and MovAlyzeR® share similar feature sets. Kinematic and static features correlate, supporting the validity of automated handwriting identification algorithms.
Area of Science:
- Forensic Science
- Biometrics
- Computer Science
Background:
- Automated handwriting identification systems lack transparency for forensic examiners.
- This research addresses the understandability gap in complex forensic handwriting analysis tools.
Purpose of the Study:
- To investigate the relationship between kinematic features (MovAlyzeR®) and static features (FLASH ID®).
- To determine if these two systems utilize similar underlying feature sets for handwriting comparison.
Main Methods:
- 33 writers produced cursive and handprinted samples of the London Letter.
- Dissimilarity scores were calculated using static (FLASH ID®) and kinematic (MovAlyzeR®) features.
- Statistical analysis explored correlations between feature sets.
Main Results:
- Kinematic spatial-geometric and temporal features showed a significant relationship with FLASH ID® scores.
- Pen pressure features did not correlate significantly.
- Similar relationships were found for both cursive and handprinted samples.
Conclusions:
- FLASH ID® and MovAlyzeR® likely rely on overlapping feature sets for handwriting analysis.
- Findings support the validity of biometric matching algorithms used in FLASH ID® based on MovAlyzeR® kinematic data.
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