This study was designed to produce the first baseline measure of reliability in bloodstain pattern classification. A panel of experienced bloodstain pattern analysts examined over 400 spatter patterns on three rigid non-absorbent surfaces. The patterns varied in spatter type and extent. A case summary accompanied each pattern that either contained neutral information, information to suggest the correct pattern (i.e., was positively biasing), or information to suggest an incorrect pattern (i.e., was negatively biasing). Across the variables under examination, 13% of classifications were erroneous. Generally speaking, where the pattern was more difficult to recognize (e.g., limited staining extent or a patterned substrate), analysts became more conservative in their judgment, opting to be inconclusive. Incorrect classifications increased as a function of the negatively biasing contextual information. The implications of the findings for practice are discussed.
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http://dx.doi.org/10.1111/1556-4029.13091 | DOI Listing |
Forensic Sci Int
December 2024
Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China. Electronic address:
Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs detected by DNA methylation microarray screening, have been widely studied for forensic body fluid identification. However, some CpGs have limited ability to distinguish certain body fluid types.
View Article and Find Full Text PDFForensic Sci Int
December 2024
Criminal Investigation School, Southwest University of Political Science and Law, Chongqing, China; Chongqing Institutions of Higher Education Municipal Key Criminal Technology Laboratory, Chongqing, China; Intelligent Research Center of Difficult Homicide Cases Investigation, Southwest University of Political Science and Law, Chongqing, China. Electronic address:
In criminal investigations, distinguishing between impact spatters and fly spots presents a challenge due to their morphological similarities. Traditional methods of bloodstain pattern analysis (BPA) rely significantly on the expertise of professional examiners, which can result in limitations including low identification efficiency, high misjudgment rates, and susceptibility to external disturbances. To enhance the accuracy and scientific rigor of identifying impact spatters and fly spots, this study employed artificial intelligence techniques in image recognition and transfer learning.
View Article and Find Full Text PDFAn expert case is presented in which a man was found dead in his apartment, on the bed. Upon examination of the crime scene, the deceased was found to have a contused wound of the frontoparietal region on the left side. The apartment contained a large number of bloodstains, including patterns characteristic of arterial spurt.
View Article and Find Full Text PDFJ Forensic Sci
December 2024
School of Health, Education, Policing and Sciences, University of Staffordshire, Stoke-on-Trent, UK.
Fire is often used to conceal or destroy evidence of violent crimes, making it essential to understand how fire environments affect forensic evidence, particularly bloodstain patterns. This study investigates the impact of high heat environments and fire on the morphology and analysis of bloodstain patterns. Using controlled fire exposure, bloodstains were analyzed pre- and post-fire exposure on various substrates, including glass, painted drywall, and painted plywood.
View Article and Find Full Text PDFData Brief
December 2024
Center for Computational & Data Sciences, Independent University, Bangladesh, Block B, Bashundhara R/A, Dhaka 1229, Bangladesh.
The FabricSpotDefect dataset is, to the best of our knowledge, the first dataset specifically designed to accurately challenge computer vision in detecting fabric spots. There are a total of 1014 raw images and manually annotated 3288 different categories of spots. This dataset expands to 2300 augmented images after applying six categories of augmentation techniques like flipping, rotating, shearing, saturation adjustment, brightness adjustment, and noise addition.
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