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

This paper is a postprint of a paper submitted to and accepted for publication in IET Biometrics and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital LibraryThis article presents a method for computing Likelihood Ratios (LR) from multimodal score distributions produced by an Automated Fingerprint Identification System (AFIS) feature extraction and comparison algorithm. The AFIS algorithm used to compare fingermarks and fingerprints was primarily developed for forensic investigation rather than for forensic evaluation. The computation of the scores is speed-optimized and performed on three different stages, each of which outputs discriminating scores of different magnitudes together forming a multimodal score distribution. It is worthy mentioning that each fingermark to fingerprint comparison performed by the AFIS algorithm results in one single similarity score (e.g. one score per comparison). The multimodal nature of the similarity scores can be typical for other biometric systems and the method proposed in this work can be applied in similar cases, where the multimodal nature in similarity scores is observed. In this work we address some of the problems related to modelling such distributions and propose solutions to issues like data sparsity, dataset shift and over-fitting. The issues mentioned affect the methods traditionally used in the situation when a multimodal nature in the similarity scores is observed (a Kernel Density Functions (KDF) was used to illustrate these issues in our case). Furthermore, the method proposed produces interpretable results in the situations when the similarity scores are sparse and traditional approaches lead to erroneous LRs of huge magnitudesThe research was conducted in scope of the BBfor2 – Marie Curie Initial Training Network (FP7-PEOPLE-ITN-2008 under the Grant Agreement 238803) at the Netherlands Forensic Institute in cooperation with the ATVS Biometric Recognition Group at the Universidad Autonoma de Madrid and the National Police Services Agency of the Netherland

Gaussianization of LA-ICP-MS features to improve calibration in forensic glass comparison

The forensic comparison of glass aims to compare a glass sample of an unknown source with a control glass sample of a known source. In this work, we use multi-elemental features from Laser Ablation Inductively Coupled Plasma with Mass Spectrometry (LA-ICP-MS) to compute a likelihood ratio. This calculation is a complex procedure that generally requires a probabilistic model including the within-source and betweensource variabilities of the features. Assuming the within-source variability to be normally distributed is a practical premise with the available data. However, the between-source variability is generally assumed to follow a much more complex distribution, typically described with a kernel density function. In this work, instead of modeling distributions with complex densities, we propose the use of simpler models and the introduction of a data pre-processing step consisting on the Gaussianization of the glass features. In this context, to obtain a better fit of the features with the Gaussian model assumptions, we explore the use of different normalization techniques of the LA-ICP-MS glass features, namely marginal Gaussianization based on histogram matching, marginal Gaussianization based on Yeo-Johnson transformation and a more complex joint Gaussianization using normalizing flows. We report an improvement in the performance of the Likelihood Ratios computed with the previously Gaussianized feature vectors, particularly relevant in their calibration, which implies a more reliable forensic glass comparisonThis work has been supported by the Spanish Ministerio de Ciencia e Innovación through grant PID2021-125943OB-I0