Improving Filler/Suspect Similarity in Eyewitness Lineups Using Facial Recognition Systems

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

Eyewitness lineups are an essential investigative tool for solving crimes. During a lineup, eyewitnesses view a group of 6-8 individuals and are instructed to indicate which, if any, of the individuals is the perpetrator of the crime. Lineups typically include only one individual whom the police view as a suspect, and other known innocents called fillers. Selection of the suspect by the eyewitness is taken as evidence of guilt and often leads to arrest and prosecution. Thus, developing procedures for conducting lineups that maximize the likelihood that eyewitnesses will select guilty suspects while minimizing the likelihood that they will erroneously select innocent suspects is crucially important for the fair administration of justice. Past research suggests that lineups where the suspect stands out from the fillers, referred to as biased lineups, reduce identification accuracy, resulting primarily from an increase in false identifications of innocent suspects. Lineups in which the suspect resembles the fillers so closely as to be hard to distinguish from them also reduce identification accuracy, making the task too difficult for otherwise reliable witnesses. Current guidelines thus instruct lineup constructors to attain a "sweet spot" of similarity where suspects are similar enough to fillers, yet not too similar. Implementing these guidelines in actual practice, however, has proven challenging because there is no universally agreed upon way of measuring filler/suspect similarity, nor has existing research clearly delineated a method to achieve the "sweet spot" of similarity that can be used in the field. The main goal of this project is to enhance eyewitness lineup constructors' ability to include fillers with appropriate levels of similarity in their lineups. This project seeks to develop foundational knowledge about how filler/suspect similarity affects eyewitness identification accuracy, and, in doing so, lays the groundwork for new tools that could enable lineup administrators to better optimize similarity to improve accuracy. The project will exploit an emergent technology--facial recognition software--being widely embraced by law enforcement agencies to create lineups, but which has heretofore been largely unexamined by eyewitness researchers. Using a novel experimental platform built upon an underlying database of over 570,000 potential filler photographs, the investigators will conduct two studies that experimentally measure how filler/suspect similarity--measured algorithmically in a manner widely accessible to other researchers and practitioners--relates to eyewitness accuracy, and how its relationship with eyewitness accuracy compares to that of traditional similarity measures. To validate their findings, the investigators will then conduct a prospective study where they apply the facial comparison algorithm to a new set of lineups to predict those most likely to generate accurate identifications, and then assess the accuracy of these predictions. Should the validation study prove successful, the project's findings could be used to develop new field tools enabling lineup administrators to more easily implement guidelines regarding filler/suspect similarity. Better adherence to these guidelines can improve the quality and accuracy of lineup identifications which will, in turn, reduce the incidence of wrongful convictions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.