2D Source area prediction based on physical characteristics of a regular, passive blood drip stain.
Violent criminal acts are often accompanied by dynamic blood shedding events. Bloodstain pattern analysis particularly deals with estimation of the dynamic blood shedding events from the static bloodstain patterns that have been left at the scene. Of all the stain patterns present at a crime scene, drip stain patterns are common stain patterns one would expect to document at a violent crime scene. The paper documents statistically significant correlations between different physical parameters, such as fall height, total number of spines associated with each stain. Statistical significant correlation between the angle of impact and the total number of spines associated with each stain pattern has been established in this work. The paper propounds that the breadth of a regular drip stain is particularly significant in making predictions empirically as also statistically about the surface area from which blood has dripped leading to the formation of a particular drip stain. A data model has been developed using machine learning techniques to predict the range of surface radius from which blood has dripped and lead to the formation of a particular drip stain (Accuracy: 97.53%, Sensitivity=0.9481, Specificity=1).
Department of Computer Science and Engineering, University of Calcutta, JD Block Salt Lake, Sector III, Kolkata, 700098, India. Electronic address: nabs.basu@gmail.com.
Nabanita
N
Basu
Department of Computer Science and Engineering, University of Calcutta, JD Block Salt Lake, Sector III, Kolkata, 700098, India. Electronic address: 1954samir@gmail.com.
Samir Kumar
SK
Bandyopadhyay
D001801:Blood Stains; D003415:Crime; D044707:Forensic Sciences; D006801:Humans; D008962:Models, Theoretical
Bloodstain patterns; Crime scene; Discriminant analysis; Drip stain; K-NN; Naïve Bayes; Prediction; SVM; Source dimension; Supervised learning