ABSTRACT
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.
Subject(s)
Dermatoglyphics , Likelihood Functions , Automation , Databases as Topic , HumansABSTRACT
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.
