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.
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.
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.
