Comparison of Combined Probabilistic Connectionist Models in a Forensic Application

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.