Crime scene classification

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 (MultipleLayer 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. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Design Methodology

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Ricardo O. Abu Hana, Cinthia Obladen de Almendra Freitas, Luiz S. Oliveira, Flávio Bortolozzi
• Keywords: Applied Computing, Crime Scenes, Neural Networks, SAC 2008, Violent Crime