A preliminary investigation into the classification of wipe and swipe bloodstain patterns between human and...

Bloodstain pattern types, such as wipes and swipes, are frequently encountered at crime scenes and can offer critical insight into the sequence of events. However, these pattern types can be difficult to reliably distinguish, highlighting the need for modern, objective approaches to classification that reduce the potential for human error. In this study, 50 participants were asked to classify 40 test bloodstain pattern images (20 wipes and 20 swipes). These same images were subsequently classified using Microsoft Azure Custom Vision (MACV), an artificial intelligence (AI) image recognition platform. The MACV model was trained using 5425 bloodstain pattern images, including impact, expirated, cessation cast-off, wipe, and swipe stains, across a range of background colors. At the 50th training iteration, the AI achieved 100% accuracy in classifying both wipe and swipe patterns, outperforming participants who achieved an average accuracy of 52% (47% for wipes and 57% for swipes), marking a 48% improvement in classification performance. The model was further trained to the 80th iteration using rotated images, achieving 98.75% accuracy on the rotated test set.