Search for: All records

Abstract The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates an additional layer of complexity. Hence, the data has a hierarchical structure in addition to the existence of underlying subpopulations. Finite mixtures are known for modeling heterogeneity; however, previous parameter estimation procedures assume that the data is generated through a simple random sampling process. We propose using a semi‐supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same source, yet an unknown subpopulation. A simulation study and a real data analysis based on famous glass datasets and a keystroke dynamic typing data set show that the proposed approach performs better than other approaches that have been used previously in practice. 

Abstract Forensic science practitioners are often called upon to attribute crimes using trace evidence, such as explosive remnants, with the ultimate goal of associating a crime with a suspect or suspects in order to prevent further attacks. The explosive charge is an attractive component for attribution in crimes involving explosives as there are limited pathways for acquisition. However, there is currently no capability to link an explosive charge to its source via post‐blast trace residues using isotope ratios or trace elements. Here, we sought to determine if pre‐blast attribution signatures are preserved after detonation and can be subsequently recovered and detected. A field study was conducted to recover samples of post‐blast explosives from controlled detonations of ammonium nitrate‐aluminum (AN‐Al), which were then analyzed via isotope ratio mass spectrometry (IRMS) and inductively coupled plasma‐mass spectrometry (ICP‐MS) for quantitation and profiling of isotopes ratio and trace element signatures, respectively. Oxygen and nitrogen isotope ratios from AN‐Al yielded some of the most promising results with considerable overlap within one standard deviation of the reference between the spreads of pre‐ and post‐blast data. Trace element results from AN‐Al support the findings in the isotope ratio data, with 26 elements detected in both pre‐ and post‐blast samples, and several elements including B, Cd, Cr, Ni, Sn, V, and Zn showing considerable overlap. These preliminary results provide a proof‐of‐concept for the development of forensic examinations that can attribute signatures from post‐blast debris to signatures in pre‐blast explosive materials for use in future investigations. 

Abstract 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. 

Prescreening is a methodology where forensic examiners select samples similar to given trace evidence to represent the background population. This background evidence helps assign a value of evidence using a likelihood ratio or Bayes factor. A key advantage of prescreening is its ability to mitigate effects from subpopulation structures within the alternative source population by isolating the relevant subpopulation. This paper examines the impact of prescreening before assigning evidence value. Extensive simulations with synthetic and real data, including trace element and fingerprint score examples, were conducted. The findings indicate that prescreening can provide an accurate evidence value in cases of subpopulation structures but may also yield more extreme or dampened evidence values within specific subpopulations. The study suggests that prescreening is beneficial for presenting evidence relative to the subpopulation of interest, provided the prescreening method and level are transparently reported alongside the evidence value.