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Comparison of Combined Probabilistic Connectionist Models in a Forensic Application

  • Edmondo Trentin
  • Luca Lusnig
  • Fabio Cavalli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7081)

Abstract

A growing interest toward automatic, computer-based tools has been spreading among forensic scientists and anthropologists wishing to extend the armamentarium of traditional statistical analysis and classification techniques. The combination of multiple paradigms is often required in order to fit the difficult, real-world scenarios involved in the area. The paper presents a comparison of combination techniques that exploit neural networks having a probabilistic interpretation within a Bayesian framework, either as models of class-posterior probabilities or as class-conditional density functions. Experiments are reported on a severe sex determination task relying on 1400 scout-view CT-scan images of human crania. It is shown that connectionist probability estimates yield higher accuracies than traditional statistical algorithms. Furthermore, the performance benefits from proper mixtures of neural models, and it turns up affected by the specific combination technique adopted.

Keywords

Multiple classifier neural net density estimation forensics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Edmondo Trentin
    • 1
  • Luca Lusnig
    • 1
  • Fabio Cavalli
    • 2
  1. 1.Dip. di Ingegneria dell’InformazioneUniversità di SienaItaly
  2. 2.Research Unit of Paleoradiology and All. Sci.AOUTS TriesteItaly

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