Abstract
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.
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Ali, T., Spreeuwers, L., Veldhuis, R. (2011). Towards Automatic Forensic Face Recognition. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25453-6_5
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DOI: https://doi.org/10.1007/978-3-642-25453-6_5
Publisher Name: Springer, Berlin, Heidelberg
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