Towards a Likelihood Ratio Approach for Bloodstain Pattern Analysis

TL;DR

This paper investigates using a likelihood ratio method to assess bloodstain patterns, modeling patterns with features derived from ellipses and estimating probabilities with Gaussian models, demonstrating feasibility with real data.

Contribution

It introduces a likelihood ratio framework for bloodstain pattern analysis using quantitative features and Gaussian modeling, advancing forensic evidence assessment methods.

Findings

Feasibility demonstrated with real impact and gunshot pattern data

Quantitative features effectively summarize bloodstain patterns

Challenges for future application identified

Abstract

In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as a collection of ellipses with each ellipses characterized by its location, size and orientation. Quantitative measures and features are derived to summarize key aspects of the patterns. A bivariate Gaussian model is chosen to estimate the distribution of features under a given hypothesis and thus approximate the likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot patterns is used to train and evaluate the model. Results demonstrate the feasibility of the likelihood ratio approach for bloodstain pattern analysis. The results also hint at some of the challenges that need to be addressed for future use of the likelihood ratio approach for bloodstain pattern analysis.