Survey of crowbar and household paints in burglary cases-population studies, transfer and interpretation

Experimental data useful for the interpretation of paint evidence recovered during burglary cases were obtained. A population study was carried out on 41 blue crowbars seized on suspects in Switzerland and 37 blue paints traces found at burglary scenes. Paint traces were also searched on the blades of 207 crowbars seized by the police in Switzerland and 24 white traces were analysed: these paints were analysed using infrared spectroscopy (FTIR) in order to estimate relative frequencies of each paint type. Simulated contacts were carried out between crowbars and painted wood in order to study the phenomenon of transfer and to evaluate the amount of paint transferred: a total of 198 simulations were carried out including individual, successive and cross transfer. The paint properties such as the chemical composition and its age influenced the amount of paint transferred. Cross transfer from the tool paint to the wood and vice versa was regularly observed. Moreover, secondary transfer of paint coming from the preceding wooden surfaces was also systematically observed: this could establish links between several burglary scenes and a suspected tool. A scenario of a burglary case involving the cross transfer between tool and household paints is proposed as a numerical example: the evaluation of such case was formalised using likelihood ratios based on the experimental data obtained.     

Resolving differing expert opinions

This paper explores procedural mechanisms to resolve differing conclusions when two experts have initially worked independently. These experts can be two human examiners or one of them may be a computer-based model. The resolving process is presented as part of the ACE-V protocol adopted widely in pattern recognition areas (e.g. fingerprints, footwear marks, toolmarks or handwritings/signatures comparisons). It set the conditions of operations and delineates a resolving process that is based on the principles of transparency and detailed argumentations. We predict a gradual but steady introduction of computer-based models in the forensic pattern recognition areas. In our opinion, the rules to resolve differing opinions ought to be articulated and documented in the form of standard operating procedures, before any deployment in casework practice.     

Computation of likelihood ratios in fingerprint identification for configurations of any number of minutiae

Recent court challenges have highlighted the need for statistical research on fingerprint identification. This paper proposes a model for computing likelihood ratios (LRs) to assess the evidential value of comparisons with any number of minutiae. The model considers minutiae type, direction and relative spatial relationships. It expands on previous work on three minutiae by adopting a spatial modeling using radial triangulation and a probabilistic distortion model for assessing the numerator of the LR. The model has been tested on a sample of 686 ulnar loops and 204 arches. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutiae data. The metric used to assess similarity between two feature vectors is based on an Euclidean distance measure. Tippett plots and rates of misleading evidence have been used as performance indicators of the model. The model has shown encouraging behavior with low rates of misleading evidence and a LR power of the model increasing significantly with the number of minutiae. The LRs that it provides are highly indicative of identity of source on a significant proportion of cases, even when considering configurations with few minutiae. In contrast with previous research, the model, in addition to minutia type and direction, incorporates spatial relationships of minutiae without introducing probabilistic independence assumptions. The model also accounts for finger distortion.     

Evidence evaluation in fingerprint comparison and automated fingerprint identification systems--modelling within finger variability

Recent challenges and errors in fingerprint identification have highlighted the need for assessing the information content of a papillary pattern in a systematic way. In particular, estimation of the statistical uncertainty associated with this type of evidence is more and more called upon. The approach used in the present study is based on the assessment of likelihood ratios (LRs). This evaluative tool weighs the likelihood of evidence given two mutually exclusive hypotheses. The computation of likelihood ratios on a database of marks of known sources (matching the unknown and non-matching the unknown mark) allows an estimation of the evidential contribution of fingerprint evidence. LRs are computed taking advantage of the scores obtained from an automated fingerprint identification system and hence are based exclusively on level II features (minutiae). The AFIS system attributes a score to any comparison (fingerprint to fingerprint, mark to mark and mark to fingerprint), used here as a proximity measure between the respective arrangements of minutiae. The numerator of the LR addresses the within finger variability and is obtained by comparing the same configurations of minutiae coming from the same source. Only comparisons where the same minutiae are visible both on the mark and on the print are therefore taken into account. The denominator of the LR is obtained by cross-comparison with a database of prints originating from non-matching sources. The estimation of the numerator of the LR is much more complex in terms of specific data requirements than the estimation of the denominator of the LR (that requires only a large database of prints from an non-associated population). Hence this paper addresses specific issues associated with the numerator or within finger variability. This study aims at answering the following questions: (1) how a database for modelling within finger variability should be acquired; (2) whether or not the visualisation technique or the choice of different minutiae arrangements may influence that modelling and (3) what is the magnitude of LRs that can be expected from such a model. Results show that within finger variability is affected by the visualisation technique used on the mark, the number of minutiae and the minutiae configuration. They also show that the rates of misleading evidence in the likelihood ratios obtained for one of the configurations examined are low.     

Computation of likelihood ratios in fingerprint identification for configurations of three minutiae

Recent challenges to fingerprint evidence have brought forward the need for peer-reviewed scientific publications to support the evidential value assessment of fingerprint. This paper proposes some research directions to gather statistical knowledge of the within-source and between-sources variability of configurations of three minutiae on fingermarks and fingerprints. This paper proposes the use of the likelihood ratio (LR) approach to assess the value of fingerprint evidence. The model explores the statistical contribution of configurations of three minutiae using Tippett plots and related measures to assess the quality of the system. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutia position, type, and direction. Spatial relationships have been coded using Delaunay triangulation. The metric, used to assess similarity between two feature vectors is based on an Euclidean distance measure. The within-source variability has been estimated using a sample of 216 fingerprints from four fingers (two donors). Between-sources variability takes advantage of a database of 818 ulnar loops from randomly selected males. The results show that the data-driven approach adopted here is robust. The magnitude of LRs obtained under the prosecution and defense propositions stresses upon the major evidential contribution that small portions of fingermark, containing three minutiae, can provide regardless of its position on the general pattern.