Experimental results on data analysis algorithms for extracting and interpreting edge feature data for duct tape and textile physical fit examinations.

A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.

Assessing physical fit examinations of stabbed and torn textiles through a large dataset of casework-like items and interlaboratory studies.

Several organizations have outlined the need for standardized methods for conducting physical fit comparisons. This study answers this call by developing and evaluating a systematic and transparent approach for examining, documenting, and interpreting textile physical fits, using qualitative feature descriptors and a quantitative metric (Edge Similarity Score, ESS) for the physical fit examination of textile materials. Here, the results from 1027 textile physical fit comparisons are reported. This includes the evaluation of inter and intraanalyst variation when using this method for hand-torn and stabbed fabrics. ESS higher than 80% and ESS lower than 20%, respectively, support fit and nonfit conclusions. The results show that analyst accuracy ranges from 88% to 100% when using this criterion. The estimated false-positive rate for this dataset (2% false positives, 10 of 477 true nonfit pairs) demonstrates the importance of assessing the quality of a physical fit during an examination and reveals that potential errors are low, but possible in textile physical fit examinations. The risk of error must be accounted for in the interpretation and verification processes. Further analysis shows that factors such as the separation method, construction, and design of the samples do not substantially influence the ESS values. Additionally, the proposed method is independently evaluated by 15 practitioners in an interlaboratory exercise that demonstrates satisfactory reproducibility between participants. The standardized terminology and documentation criteria are the first steps toward validating approaches to streamline the peer review process, minimize bias and subjectivity, and convey the probative value of the evidence.

Using convolutional neural networks to support examiners in duct tape physical fit comparisons.

This paper describes the construction and use of a machine-learning model to provide objective support for a physical fit examination of duct tapes. We present the ForensicFit package that can preprocess and database raw tape images. Using the processed tape image, we trained a convolutional neural network to compare tape edges and predict membership scores (i.e., fit or non-fit category). A dataset of nearly 2000 tapes and 4000 images was evaluated, including various quality grades: low, medium, and high, as well as two separation methods, scissor-cut and hand-torn. The model predicts medium-quality and high-quality scissor-cut tape more accurately than hand-torn, whereas for low-quality tape predicts the hand-torn tapes more accurately. These results are consistent with previous studies performed on the same datasets by analyst examinations. A method of pixel importance was also implemented to show which pixels are used to make the decision. This method can confirm some fit features that correspond with analyst-identified features, like edge morphology and backing pattern. This pilot study demonstrates the feasibility of computational algorithms to build physical fit databases and automated comparisons using deep neural networks, which can be used as a model for other materials.

Assessing significant factors that can influence physical fit examinations - Part I. Physical fits of torn and cut duct tapes.

This study expands upon a previously developed method that quantifies the similarity of the compared tape edges by systematically studying the effect of several separation methods and tape grades on the quality of a fit. Analysts examined more than 3300 pairs of hand-torn or scissor-cut duct tapes from three different tape grades while they were kept blind from the ground truth to minimize bias. The samples were examined following a three-step methodology: 1) qualitative assessment of the overall edge alignment and description of edge pattern, 2) macroscopic evaluation of the edges' features, 3) bin by bin subunit assessment of tape edges and estimation of the edge similarity score. A report template was designed to maintain records of the decision-making process. In the second and third steps, eight comparison features were defined and documented using auto-populated cell options. Generally, misidentification rates were low, with no false positives reported. Coinciding with previous research, low scores (under 20%) provided the most support for a non-fit conclusion, while high scores (80% or higher) supported a fit conclusion. A statistical analysis of the separation method and quality of tape revealed a potential interaction between these factors and showed that they significantly impact the edge scores for true fitting pairs, but not the true non-fits' scores. The developed comparison and documentation criteria can assist practitioners with a more straightforward, consistent, and transparent interpretation and reporting approach.

Forensic physical fits in the trace evidence discipline: A review.

Physical fit examinations have long played a critical role in forensic science, particularly in the trace evidence, toolmark, and questioned documents disciplines. Specifically, in trace evidence, physical fits arise in various instances such as separated pieces of duct tape, torn textile fragments, and fractured polymeric items to name a few. The case report and research basis for forensic physical fit dates to the late 1700s and varies by material type. Three main areas of physical fit appear within the literature: case reports, fractography studies, and quantitative assessment of a fracture fit. A strong foundation within the discipline lies in case reports, articles demonstrating occurrences of physical fit the authors have experienced in their laboratories. Fractography research offers information about the fracturing mechanism of a given material for purposes of identifying a potential breaking source. Also, fractography studies demonstrate variation in fracture morphology per material types, with a qualitative basis for comparison and reporting. The current shift in the research appears to be more quantitative or performance-based, assessing the error rates associated with physical fit examinations, the application of likelihood ratios as a means to determine evidential weight, probabilistic interpretations of large sample sets, and the implementation of automatic edge-detection algorithms to support the examiner's expert opinion. This review aims to establish the current state of physical fit research through what has been accomplished, the limitations faced due to the unpredictable nature of casework, and the future directions of the discipline. In addition, current practice in the field is evaluated through a review of standard operating procedures.

Development and validation of a systematic approach for the quantitative assessment of the quality of duct tape physical fits.

Duct tape is a common type material found at crime scenes such as sexual assaults, murders, kidnappings, and bombings. During the examination of a known and questioned item, a 3D realignment along their edges is known as a physical fit and is often regarded as conclusive evidence that the items were once part of a single object. The conclusion of a fit between edges relies on the examiner's judgment to identify distinctive features across the tape ends. However, there are currently no consensus-based methodologies or standards to inform their opinions. This study developed a practical method to qualify and quantify tape end match features using edge similarity scores (ESS) and provided an empirically demonstrable basis to assess the significance of duct tape fracture fits. ESS were calculated as the proportion of observed matching sections per scrim bins across the fractured edge, providing a quantifiable criterion and means for a systematic peer review process. A set of 2280 duct tape end comparisons were analyzed for the validation study. The probative value of physical fits was evaluated through similarity metrics, error rates, and score-based likelihood ratios. The effects of separation method, stretching, and tape grade on the distribution of ESS and the overall accuracy are reported. The accuracy ranged from 84.9 % (higher quality hand-torn set) to over 99 % (low and mid-quality sets). No false positives were reported for any of the sets examined. On average, ESS higher than 80 % provided a score likelihood ratio (SLR) that supported the conclusion of a match, and ESS lower than 25 % provided an SLR supporting the conclusion of non-match.