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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
PubMed
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.

Keywords:
automated handwriting systemblack box systemhandwritingquestioned documentsstatistical modelingvaliditywhite box system

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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.