Use of an Automated System to Evaluate Feature Dissimilarities in Handwriting Under a Two‐Stage Evaluative Process*, †. Issue 6 (24th August 2020)

• Record Type: Journal Article
• Authors: Fuglsby, Cami; Saunders, Christopher; Ommen, Danica M.; Caligiuri, Michael P.
• Is Part Of: Journal of forensic sciences. Volume 65:Issue 6(2020)
• Page Start: 2080
• Page End: 2086
• Publication Date: 2020-08-24
• Subjects: automated systems -- handwriting -- handprinting -- two‐stage approach -- evidence interpretation -- questioned documents; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.14547
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: The two‐stage evaluative process is an established framework utilized by forensic document examiners (FDEs) for reaching a conclusion about the source(s) of handwritten evidence. In the second, or discrimination, stage, the examiner attempts to estimate the rarity of observations in a relevant background population. Unfortunately, control samples from a relevant background population are often unavailable, leaving the FDE to reach this determination based on subjective experience. Automated handwriting feature recognition systems are capable of performing both feature comparison and discrimination, yet these systems have not been subjected to empirical validation studies. In the present study, we repurposed a commercially available automated system to generate empirical distributions for ranking feature dissimilarity scores among pairs of handwritten phrases. The blinded results of this automated process were used to survey an international cohort of 36 FDEs regarding their strength of support for same‐ and different‐writer propositions. The survey served to cross‐validate FDE decision‐making under the two‐stage approach. Results from the survey demonstrated a clear pattern of response consistent with ground truth. Predictive regression analyses indicated that the automated feature dissimilarity scores and the log of their cumulative distribution functions accounted for 72% of the variability in FDE opinions. This study demonstrated that feature dissimilarity scores acquired using automated processes and their distributions are closely aligned with FDE decision‐making processes supporting the heuristic value of the two‐stage evaluative framework.