A previous paper published in this journal proposed a model for evaluating the location of fingermarks on two-dimensional items (de Ronde, van Aken, de Puit and de Poot (2019)). In this paper, we apply the proposed model to a dataset consisting of letters to test whether the activity of writing a letter can be distinguished from the alternative activity of reading a letter based on the location of the fingermarks on the letters. An experiment was conducted in which participants were asked to read a letter and write a letter as separate activities on A4- and A5-sized papers. The fingermarks on the letters were visualized, and the resulting images were transformed into grid representations. A binary classification model was used to classify the letters into the activities of reading and writing based on the location of the fingermarks in the grid representations. Furthermore, the limitations of the model were studied by testing the influence of the length of the letter, the right- or left-handedness of the donor and the size of the paper with an additional activity of folding the paper. The results show that the model can predict the activities of reading or writing a letter based on the fingermark locations on A4-sized letters of right-handed donors with 98 % accuracy. Additionally, the length of the written letter and the handedness of the donor did not influence the performance of the classification model. Changing the size of the letters and adding an activity of folding the paper after writing on it decreased the model’s accuracy. Expanding the training set with part of this new set had a positive influence on the model’s accuracy. The results demonstrate that the model proposed by de Ronde, van Aken, de Puit and de Poot (2019) can indeed be applied to other two-dimensional items on which the disputed activities would be expected to lead to different fingermark locations. Moreover, we show that the location of fingermarks on letters provides valuable information about the activity that is carried out.
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In this paper, we describe a promising method to evaluate the location of fingermarks on two-dimensional objects, which provides valuable information for the evaluation of fingermarks at activity level. For this purpose, an experiment with pillowcases was conducted at the Dutch music festival Lowlands, to test whether the activity ‘smothering’ can be distinguished from an alternative activity like ‘changing a pillowcase’ based on the touch traces on pillowcases left by the activities. Participants performed two activities with paint on their hands: smothering a victim with the use of a pillow and changing a pillowcase of a pillow. The pillowcases were photographed and translated into grid representations. A binary classification model was used to classify the pillowcases into one of the two classes of smothering and changing, based on the distance between the grid representations. After applying the fitted model to a test set, we obtained an accuracy of 98.8%. The model showed that the pillowcases could be well separated into the two classes of smothering and changing, based on the location of the fingermarks. The proposed method can be applied to fingermark traces on all two-dimensional items for which we expect that different activities will lead to different fingermark locations.
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Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
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Residential burglaries often go unsolved, as collected DNA traces and fingermarks frequently originate from residents rather than the offender. It is therefore important to know how to target sampling locations that specifically relate to the burglary event. However, data that aid in assessing the likelihood of a burglar touching certain surfaces, and, consequently leaving trace evidence, is unavailable. Instead, forensic examiners rely primarily on their personal experience and expertise to determine where burglary-related traces are most likely to be found.The current study aims to identify specific areas that are contacted during different types of interactions with points of entry. An experiment was conducted at a Dutch music festival, where participants simulated both a legitimate and burglary scenario. Using paint, the points of contact between the participants’ hands and the experimental set-up were recorded. The contact locations of all participants were combined using heatmaps to reveal the patterns of contact. We found that different burglary methods lead to distinct contact patterns, indicating specific areas where traces are most likely to be deposited. Our findings can support forensic examiners in making evidence-based decisions during search strategies in burglary investigations.
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Selecting the most successful and relevant traces at the crime scene is of the utmost importance as irrelevant traces may take up valuable capacity, increasing the risk that cases remain unsolved. In order to ensure that most relevant traces are collected, knowledge is required regarding how and where forensic traces are deposited given the activities being considered in the case relevant scenarios. As of now, however, this knowledge is limited. While studies on the transfer, prevalence, persistence and recovery of trace materials are becoming more common, most of such studies focus on crime centered scenarios and often do not take alternative, innocent, scenarios into account. Moreover, these studies often focus on specific objects or small spaces such as offices or vehicles. With the project No trace to waste (start: January 2023), we aim to extend the knowledge on trace dynamics as a strategy to improve the search for and selection of most relevant and successful traces. Throughout the project, the focus will be on DNA and fingermarks within home environments, as burglaries and home invasions remain common offenses and can be considered highly impactful on the victim’s lives. During the first phase, the distribution of traces given certain scenarios is investigated. Test subjects are asked to simulate various, both innocent and crime-related, activities within the model homes of the Dutch Police Academy. A fluorescent tracer is used to determine the objects/surfaces which the test subject has been in contact with and thus, where trace material could be expected to be deposited. Using these findings, the project moves onwards to investigate the persistence and transfer of actual traces during social settings, the prevalence of residents’ own DNA and fingermarks in the average active home and how these influence the recovery of crime-related traces post-offense. Finally, the project aims at supporting the forensic practitioners of the future. The knowledge obtained will be used to evaluate how to improve current practices and professional education. This in turn will support forensic practitioners on-scene with the selection of relevant traces and sampling locations and allows for a more efficient use of the available capacity and resources.
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