Advances toward validating examiner writership opinion based on handwriting kinematics

Ommen, Danica M.; Fuglsby, Cami; Caligiuri, Michael P.

doi:10.1016/j.forsciint.2020.110644

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…The study recruited 33 volunteer writers from the San Diego Sheriff's Crime Laboratory; each subject was asked to write six phrases from the London Letter [15] and to repeat each phrase five times using both handprinting and cursive writing styles (for a total of 60 writing samples per subject). Handwriting data from these subjects were used in two prior studies aimed at further understanding the decision‐making process of forensic document examiners [16,17]. Subjects were asked to write the handwriting sample phrases with an inking pen on lined papers placed on top of a Wacom (Intuos Pro, model PTH‐660) digitizing tablet.…”

Section: Methodsmentioning

confidence: 99%

“…These features characterize handwriting movement in multiple dimensions. The multidimensional kinematic features were transformed into a single score representing the dissimilarity between two handwriting samples, as in Ommen et al [17]. First, using the kinematic features for all upstrokes in a pair of handwriting samples, a dissimilarity score is constructed by determining the direction of maximum separation by applying linear discriminant analysis (LDA).…”

Section: Movalyzer ® Kinematic Feature Dissimilarity Scoresmentioning

confidence: 99%

“…For each of these seven sets of measurements (one FLASH ID ® score and six kinematic feature scores), we developed a pairwise dissimilarity score to represent a document-level comparison. Following Ommen et al [17], the pairwise dissimilarity is computed using a modification of the WDS (see the Appendix for further details). The goal was to create six different regression models to assess the marginal relationship between the MovAlyzeR ® features and the FLASH ID ® features, where the WDS for one of the six kinematic feature sets is used as the explanatory variable and the FLASH ID ® dissimilarity score is used as the response variable.…”

Section: Regression Models Of Pairwise Comparisonsmentioning

confidence: 99%

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Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons

Fuglsby

Saunders

Ommen

et al. 2021

Journal of Forensic Sciences

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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®, 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. 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® 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® 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.

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Section: Methodsmentioning

confidence: 99%

Section: Movalyzer ® Kinematic Feature Dissimilarity Scoresmentioning

confidence: 99%

Section: Regression Models Of Pairwise Comparisonsmentioning

confidence: 99%

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Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons

Fuglsby

Saunders

Ommen

et al. 2021

Journal of Forensic Sciences

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“…The Report of the Expert Working Group for Human Factors in Handwriting Examination published the report Forensic Handwriting Examination and Human Factors: Improving the Practice through a Systems Approach [ 845 ]. A further nine articles [ [846] , [847] , [848] , [849] , [850] , [851] , [852] , [853] , [854] ], and 24 conference presentations [ [855] , [856] , [857] , [858] , [859] , [860] , [861] , [862] , [863] , [864] , [865] , [866] , [867] , [868] , [869] , [870] , [871] , [872] , [873] , [874] , [875] , [876] , [877] , [878] ], including one workshop on decision-making and cognitive bias in FHE signature comparisons, comprise this section, most of which spawned from the HFHE study.…”

Section: Quality Assurancementioning

confidence: 99%

“…Ommen et al [ 848 ] set out to establish the scientific validity of expert writership opinions and the two-stage approach to evidence interpretation using measures derived from research on handwriting motor control. This comprehensive study involved 33 subjects writing six phrases using an inking pen on paper placed atop a digitizing tablet to record x, y, and z data at a capture rate of 100 measurements/second.…”

Section: Quality Assurancementioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

Ospina,

Costa,

Rêgo

et al. 2024

Statistical Analysis

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This work explores the use of nonparametric quantifiers in the signature verification problem of handwritten signatures. We used the MCYT‐100 (MCYT Fingerprint subcorpus) database, widely used in signature verification problems. The discrete‐time sequence positions in the x ‐axis and y‐axis provided in the database are preprocessed, and time causal information based on nonparametric quantifiers such as entropy, complexity, Fisher information, and trend are employed. The study also proposes to evaluate these quantifiers with the time series obtained, applying the first and second derivatives of each sequence position to evaluate the dynamic behavior by looking at their velocity and acceleration regimes, respectively. The signatures in the MCYT‐100 database are classified via Logistic Regression, Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The quantifiers were used as input features to train the classifiers. To assess the ability and impact of nonparametric quantifiers to distinguish forgery and genuine signatures, we used variable selection criteria, such as: information gain, analysis of variance, and variance inflation factor. The performance of classifiers was evaluated by measures of classification error such as specificity and area under the curve. The results show that the SVM and XGBoost classifiers present the best performance.

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Advances toward validating examiner writership opinion based on handwriting kinematics

Cited by 8 publications

References 10 publications

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

Interpol questioned documents review 2019–2022

Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

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