access icon free Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingermarks and fingerprints were primarily developed for forensic investigation rather than for forensic evaluation purposes. Thus, in some of those algorithms, the computation of discriminating scores is speed-optimised. In the case of the AFIS algorithm used in this work, the speed-optimisation is achieved by performing the comparison in three different stages, each of which outputs scores of different magnitudes. As a consequence, all scores together present a multimodal distribution, even though each fingermark-to-fingerprint comparison generates one single score. This multimodal distribution of scores might be typical for other biometric systems or other algorithms, and the method proposed in this work can be also applied to those cases. As a result, the authors propose a probabilistic model for LR computation that presents more robustness to overfitting and data sparsity than other traditional approaches, like the use of models based on kernel density functions.

Inspec keywords: image forensics; statistical distributions; probability; optimisation; fingerprint identification

Other keywords: likelihood ratio methods; speed-optimisation; biometric systems; LR computation; forensic evidence evaluation; data sparsity; AFIS algorithms; kernel density functions; commercial off-the-shelf automated fingerprint identification systems; probabilistic model; multimodal score distributions

Subjects: Optimisation techniques; Other topics in statistics; Optimisation techniques; Image recognition; Computer vision and image processing techniques; Other topics in statistics

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