Fingerprint Identification: Potential Sources of Error and the Cause of Wrongful Convictions

Fingerprint identification has long been used by law enforcement to either identify or eliminate potential suspects in a case. It relies on friction ridges – the upraised skin that forms grooves on fingers – and friction ridge impressions, which form from natural secretions of sweat and other trace components. Latent prints, a common term for friction ridge impressions, have many benefits and advantages as a type of forensic evidence. However, they are not a perfect tool: wrongful convictions identified by post-conviction DNA testing and the re-evaluation of forensic evidence have spawned criticism and investigation into the scientific basis of this branch of forensics. This literature review examines literature in both the scientific and legal fields, and investigates three main themes: the principle of uniqueness assumed in individualization, the presence of cognitive bias and human error in analysis, and the changing role of expert testimony in court. There are arguments both for and against uniqueness, but it is still difficult to prove using statistical models and data analysis. Bias in examiners, on the other hand, undeniably exists in different ways, and should be actively guarded against in fingerprint analysis and expert testimony. Expert witness testimony that misleads, exaggerates, or is scientifically unsupportable has been linked to wrongful convictions in the past, highlighting the importance of careful regulation of how an expert witness is advised to testify. In addition to these topics, the techniques of collecting latent print evidence and the standard procedures of analysis have also been examined and evaluated for potential sources of error. Le maintien de l’ordre public utilise depuis longtemps les empreintes digitales pour identifier et éliminer des suspects d’une affaire criminelle. Les empreintes digitales se ent aux crêtes papillaires — les crêtes et les creux qui formes des rainures sur les doigts — et des empreintes des crêtes papillaires, ce qui se forme par les sécrétions naturelles de transpiration et autres composantes de traces. Les empreintes latentes, un terme courant pour les empreintes digitales, possèdent plusieurs avantages en tant qu’élément médico-légal de preuve. Toutefois, ce n’est pas une ressource able; des condamnations injustifiées identifiées par un test d’ADN post-condamnatoire et la réévaluation de l’évidence médico-légale ont frayé des critiques et des enquêtes de la base des sciences des empreintes digitales. Cette revue examine les textes dans les domaines scientifiques et médico-légaux, et examine trois thèmes : le principe d’unicité assumé par l’individualisation, la présence d’un biais cognitif et l’erreur humaine dans l’analyse, et le rôle changeant de témoignages experts devant la Cour. Il existe des arguments pour et contre l’unicité, mais l’unicité est tout de même difficile à prouver en utilisant les modèles statistiques et l’analyse de données. Un préjugé chez les examinateurs, d’autres parts, existe incontestablement, et devrait être activement évité lors de l’analyse d’empreinte digitale et de témoignages experts. Le témoignage d’expert qui induit en erreur, qui est exagéré ou qui est scientifiquement faux a mené à des condamnations injusti ées dans le passé, ce qui met en évidence l’importance d’une législation prudente sur comment l’expert est conseillé de témoigner. En plus de ces thèmes, les techniques de collecte des empreintes digitales latentes et les procédures normales d’analyse ont aussi été examinés et évalués pour des sources d’erreurs potentielles.

AFIS Based Likelihood Ratios for Latent Fingerprint Comparisons

Latent fingerprints are one of the most common pieces of evidence found on a crime scene and represent accidental or unintentional prints collected as part of a criminal investigation. They are caused when the friction ridge skin comes in contact with a surface, and thus requires the use of chemical processing to be visualized with the naked eye. The comparison and identification of fingerprints depends on various factors such as the substrate quality, surface, duration, environmental factors and examiner experience. These factors can result in reduced clarity or content, and can even cause distortions as compared to a fingerprint taken under controlled conditions. Since the release of the National Academy of Sciences (NAS) report in 2009, the field of fingerprint analysis has come under much scrutiny. Specifically, the need for more research into the determination of the accuracy and reliability of the identifications made by fingerprint examiners has been raised.;One such method used for the comparison of latent fingerprint to known prints is through an Automated Fingerprint Identification System (AFIS). The AFIS used in this research was the AFIX Tracker R where where variables were assessed: match score, match minutiae, match status, delta match score and marked minutiae, to determine which variable(s) was a better indicator of a true match. Bayesian networks were then constructed to compute the likelihood ratios to evaluate the dependency of the variables on one another,where the performance of the likelihood ratios in determining the identity of the unknown latent was assessed using Tippett and ECE plots. Receiver Operating Characteristic (ROC) curves and Bayesian networks were constructed to perform statistical analysis of the matches obtained while comparing a latent print to a ten-print card. A combination of Tippett and Empirical Cross Entropy (ECE) plots were used to assess the performance of the AFIX Tracker R in classifying unknown prints. It was observed that a match minutiae of 15 or higher resulted in a 100% true match result whereas for the non-matches,no more than 13 match minutiae were found. Moreover, the delta match scores difference between the matches and non-matches were notable (delta score of 0.1-153 for matches compared to a score of 0-0.1 for the non-matches). Overall, it was determined that approximately 87% of the time a randomly selected known match would have a higher number of match minutiae as compared to a non-match