What fingermarks reveal about activities

Fingermarks play important role in forensic science. Based on the ridge detail information present in a fingermark, individualization or exclusion of a donor is possible by comparing a fingermark obtained from a crime scene to a reference fingerprint. In this process, the intrinsic features of a fingermark are used to determine the source of the fingermark. However, in some cases, the source of a fingermark is not argued but the activity that led to the deposition of the fingermark. The question changes from ‘Who left the fingermark?’ to ‘How did the fingermark end up on the surface?’ which requires a different assessment of the findings. The aim of this dissertation is to determine how fingermarks could provide information about activities in a reliable way, in order to be used in the forensic evidence process. To answer this main research question, several studies were conducted which are described in Chapters 2 to 5 of this dissertation. Chapter 2 describes the development of a general framework to evaluate fingermarks given activity level propositions. Relevant variables that function as sources of information when evaluating fingermarks given activity level proposition were identified. Based on these variables, three Bayesian networks were presented for different evaluations of the fingermarks given activity level propositions in a case example. The presented networks function as a general framework for the evaluation of fingermarks given activity level propositions, which can be adapted to specific case circumstances. Chapter 3 shows how the proposed framework in Chapter 2 can be used in casework by showing a case example. In order to use a Bayesian network, probabilities need to be assigned to the Bayesian network. In this study, a case specific experiment with the use of knives was conducted and the resulting data was used to assign probabilities to two Bayesian networks, both focusing on a different use of the experimental data. This study has shown how different uses of the data resulting from a case specific experiment on fingermarks can be used to assign probabilities to Bayesian networks for the evaluation of fingermarks given activity level propositions. In Chapter 4, we focus on the location of fingermarks on an item. In this study, we developed a classification model to evaluate the location of fingermarks given activity level propositions based on an experiment with pillowcases. The results showed that fingermark patterns left on a pillowcase by smothering with a pillow can be well distinguished from fingermark patterns left by changing a pillowcase of a pillow. The result of this study is a model that can be used to study the location of fingermarks on two-dimensional items in general, for which is expected that different activities will lead to different trace locations. Chapter 5 investigates the application of the location model presented in Chapter 4 to a dataset of letters, to study whether the model could also be used to distinguish between fingermark patterns left when writing a letter and fingermark patterns left when reading a letter. Based on the results of this study we conclude that the model proposed in Chapter 4 is indeed applicable to other objects for which it is expected that different activities lead to different fingermark locations, given the condition that the training set is representative for the object to be tested with regards to the size of the object and the activity that was carried out with the object. This dissertation supports the view that fingermarks contain valuable information about the activity that caused the deposition of the fingermarks and provides the forensic community with reliable methods that can be used when evaluating fingermarks given activity level propositions

What fingermarks reveal about activities

Dissertation concerning the evaluation of fingermarks given activity level propositions to determine what fingermarks reveal about activities

What fingermarks reveal about activities

Fingermarks play important role in forensic science. Based on the ridge detail information present in a fingermark, individualization or exclusion of a donor is possible by comparing a fingermark obtained from a crime scene to a reference fingerprint. In this process, the intrinsic features of a fingermark are used to determine the source of the fingermark. However, in some cases, the source of a fingermark is not argued but the activity that led to the deposition of the fingermark. The question changes from ‘Who left the fingermark?’ to ‘How did the fingermark end up on the surface?’ which requires a different assessment of the findings. The aim of this dissertation is to determine how fingermarks could provide information about activities in a reliable way, in order to be used in the forensic evidence process. To answer this main research question, several studies were conducted which are described in Chapters 2 to 5 of this dissertation. Chapter 2 describes the development of a general framework to evaluate fingermarks given activity level propositions. Relevant variables that function as sources of information when evaluating fingermarks given activity level proposition were identified. Based on these variables, three Bayesian networks were presented for different evaluations of the fingermarks given activity level propositions in a case example. The presented networks function as a general framework for the evaluation of fingermarks given activity level propositions, which can be adapted to specific case circumstances. Chapter 3 shows how the proposed framework in Chapter 2 can be used in casework by showing a case example. In order to use a Bayesian network, probabilities need to be assigned to the Bayesian network. In this study, a case specific experiment with the use of knives was conducted and the resulting data was used to assign probabilities to two Bayesian networks, both focusing on a different use of the experimental data. This study has shown how different uses of the data resulting from a case specific experiment on fingermarks can be used to assign probabilities to Bayesian networks for the evaluation of fingermarks given activity level propositions. In Chapter 4, we focus on the location of fingermarks on an item. In this study, we developed a classification model to evaluate the location of fingermarks given activity level propositions based on an experiment with pillowcases. The results showed that fingermark patterns left on a pillowcase by smothering with a pillow can be well distinguished from fingermark patterns left by changing a pillowcase of a pillow. The result of this study is a model that can be used to study the location of fingermarks on two-dimensional items in general, for which is expected that different activities will lead to different trace locations. Chapter 5 investigates the application of the location model presented in Chapter 4 to a dataset of letters, to study whether the model could also be used to distinguish between fingermark patterns left when writing a letter and fingermark patterns left when reading a letter. Based on the results of this study we conclude that the model proposed in Chapter 4 is indeed applicable to other objects for which it is expected that different activities lead to different fingermark locations, given the condition that the training set is representative for the object to be tested with regards to the size of the object and the activity that was carried out with the object. This dissertation supports the view that fingermarks contain valuable information about the activity that caused the deposition of the fingermarks and provides the forensic community with reliable methods that can be used when evaluating fingermarks given activity level propositions

A study into fingermarks at activity level on pillowcases

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.</p

Using case specific experiments to evaluate fingermarks on knives given activity level propositions

Bayesian networks have shown to be a useful tool for the evaluation of forensic findings given activity level propositions. In this paper, we demonstrate how case specific experiments can be used to assign probabilities to the states of the nodes of a Bayesian network for the evaluation of fingermarks given activity level propositions. The transfer, persistence and recovery of fingermarks on knives is studied in experiments where a knife is either used to stab a victim or to cut food, representing the activities that were disputed in the case of the murder of Meredith Kercher. Two Bayesian networks are constructed, exploring the effect of different uses of the experimental data by assigning the probabilities based on the results of the experiments. The evaluation of the findings using the Bayesian networks demonstrates the potential for fingermarks in addressing activity level propositions

Using case specific experiments to evaluate fingermarks on knives given activity level propositions

Bayesian networks have shown to be a useful tool for the evaluation of forensic findings given activity level propositions. In this paper, we demonstrate how case specific experiments can be used to assign probabilities to the states of the nodes of a Bayesian network for the evaluation of fingermarks given activity level propositions. The transfer, persistence and recovery of fingermarks on knives is studied in experiments where a knife is either used to stab a victim or to cut food, representing the activities that were disputed in the case of the murder of Meredith Kercher. Two Bayesian networks are constructed, exploring the effect of different uses of the experimental data by assigning the probabilities based on the results of the experiments. The evaluation of the findings using the Bayesian networks demonstrates the potential for fingermarks in addressing activity level propositions.</p

The evaluation of fingermarks given activity level propositions

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

A study into evaluating the location of fingermarks on letters given activity level propositions

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.</p

Using case specific experiments to evaluate fingermarks on knives given activity level propositions

Bayesian networks have shown to be a useful tool for the evaluation of forensic findings given activity level propositions. In this paper, we demonstrate how case specific experiments can be used to assign probabilities to the states of the nodes of a Bayesian network for the evaluation of fingermarks given activity level propositions. The transfer, persistence and recovery of fingermarks on knives is studied in experiments where a knife is either used to stab a victim or to cut food, representing the activities that were disputed in the case of the murder of Meredith Kercher. Two Bayesian networks are constructed, exploring the effect of different uses of the experimental data by assigning the probabilities based on the results of the experiments. The evaluation of the findings using the Bayesian networks demonstrates the potential for fingermarks in addressing activity level propositions.ChemE/Advanced Soft Matte

The evaluation of fingermarks given activity level propositions

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 the 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.OLD ChemE/Organic Materials and Interface