Crime scene classification

Authors:
Ricardo O. Abu Hana;Cinthia O. A. Freitas;Luiz S. Oliveira;Flávio Bortolozzi
Affiliations:
Pontifical Catholic University of Paraná (PUCPR) -- Curitiba -- PR --Brazil;Pontifical Catholic University of Paraná (PUCPR) -- Curitiba -- PR --Brazil;Pontifical Catholic University of Paraná (PUCPR) -- Curitiba -- PR --Brazil;OPET College (OPET) -- Curitiba -- PR -- Brazil
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

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Abstract

In this paper we provide a study about crime scenes and its features used in criminal investigations. We argue that the crime scene provides a large set of features that can be used to corroborate the conclusions emitted by the experts. We also propose a set of features to classify the violent crime considering two classes: attack from inside or outside of the scene. The classification stage is based on conventional MLP (Multiple-Layer Perceptron) Neural Network and SVM (Support Vector Machine). The experimental results reveal an error rate of 30.3% (MLP), 22.8% (SVM-linear), and 19.4% (SVM-polynomial) using a database composed of 400 crime scenes.