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Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques
Abstract
Crime scene reconstruction based on circumstantial evidence and bloodstain patterns at the scene is often affected by unwanted expert bias. Using features such as bloodstain pattern, wound analysis, size of bloodstains on objects etc., predictions could be made about the relative position of the victim/s, bystander/s and perpetrator/s. Supervised learning techniques can be used to make predictions related to the murder weapon used. Gender of an individual could also be estimated from the bloody broken plastic footprint of an individual using a suitable dataset and supervised classifier. These intermediate prediction modules are important for development of event segments. The event segments add up towards the development of the events that transpired at the crime scene. An optimal sequence of events that might have transpired at the crime scene could thereby be developed using event timestamp and logical sequencing of similar incidents that had occurred in the past using probability theory.
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