An automated approach to the classification of impact spatter and cast-off bloodstain patterns

In the forensic discipline of bloodstain pattern analysis, it has been suggested that there is a blurred boundary between characterising the features of a bloodstain pattern and determining the mechanism(s) that led to its deposition. This study proposes that bloodstain pattern classification can become a distinct and logical process by implementing an automated approach. To do this, an automated bloodstain pattern recognition system was developed to enable the distinction of two types of spatter bloodstain patterns. First, global pattern features based on common bloodstain pattern properties were extracted from laboratory-generated impact spatter and cast-off bloodstain patterns. Following this, automated feature selection methods were used to identify the combination of features that best distinguished the two bloodstain pattern types. This eventually led to the training and testing of a Fisher quadratic discriminant classifier using separate subsets of the generated bloodstain patterns. When applied to the training dataset, a 100% classification precision resulted. An independent dataset comprising of bloodstain patterns generated on paint and wallpaper substrates were used to validate the performance of the classifier. An error rate of 2% was obtained when the classifier was applied to these bloodstain patterns. This automated bloodstain pattern recognition system offers considerable promise as an objective classification methodology which up to now, the discipline has lacked. With further refinement, including testing it over a wider range of bloodstain patterns, it could provide valuable quantitative data to support analysts in their task of classifying bloodstain patterns. (c) 2018 Elsevier B.V. All rights reserved.