In this paper we present a methodology and experimental results for evidence evaluation in the context of forensic face recognition. In forensic applications, the matching score (hereafter referred to as similarity score) from a biometric system must be represented as a Likelihood Ratio (LR). In our experiments we consider the face recognition system as a ‘black box’ and compute LR from similarity scores. The proposed approach is in accordance with the Bayesian framework where the duty of a forensic scientist is to compute LR from biometric evidence which is then incorporated with prior knowledge of the case by the judge or jury. In our experiments we use a total of 2878 images of 100 subjects from two different databases. Our experimental results prove the feasibility of our approach to reach a LR value given an image of a suspect face and questioned face. In addition, we compare the performance of two biometric face recognition systems in forensic casework.
|Name||Communications in Computer and Information Science|
|Conference||International Conference on Informatics Engineering and Information Science (ICIEIS 2011), Kuala Lumpur, Malaysia|
|Period||1/11/11 → …|
- Bayesian framework
- EC Grant Agreement nr.: FP7/238803
- Likelihood Ratio
- Similarity score