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

目击者指认是破案必不可少的调查工具。在指认过程中，目击者看到一组6-8个人，并被指示指出，如果有的话，谁是犯罪的肇事者。列队的人通常只有一个被警方视为嫌疑人的人，其他已知的无辜者被称为替死鬼。目击者选择嫌疑人被视为有罪的证据，常常导致逮捕和起诉。因此，制定指认程序，最大限度地提高目击证人选择有罪嫌疑人的可能性，同时最大限度地降低他们错误选择无辜嫌疑人的可能性，这对公平司法至关重要。过去的研究表明，嫌疑人从填充者中脱颖而出的队列，被称为有偏见的队列，降低了识别的准确性，主要原因是无辜嫌疑人的错误识别增加了。如果嫌疑人排成一排，与填充物非常相似，以至于难以区分，这也会降低识别的准确性，使原本可靠的证人难以完成任务。因此，当前的指导方针要求阵容构造器达到相似度的“最佳点”，即嫌疑人与填充者足够相似，但又不太相似。然而，在实际实践中实施这些指导方针已被证明具有挑战性，因为没有普遍同意的测量填充/可疑相似性的方法，现有的研究也没有明确描述一种方法来实现可在该领域使用的相似性的“最佳点”。这个项目的主要目标是增强目击者阵容构建者的能力，在他们的阵容中包括具有适当相似程度的填充者。该项目旨在发展关于填充物/嫌疑人相似性如何影响目击者识别准确性的基础知识，并在此过程中为新工具奠定基础，这些工具可以使阵容管理员更好地优化相似性以提高准确性。该项目将利用一项新兴技术——面部识别软件，该技术被执法机构广泛采用，用于创建嫌疑人阵容，但迄今为止，目击者研究人员在很大程度上尚未对其进行研究。使用基于超过570,000张潜在填充照片的底层数据库的新型实验平台，研究人员将进行两项研究，实验测量填充/可疑相似性-以其他研究人员和从业人员广泛访问的方式通过算法测量-与目击者准确性的关系，以及其与目击者准确性的关系如何与传统相似性测量相比较。为了验证他们的发现，研究人员将进行一项前瞻性研究，他们将面部比较算法应用于一组新的阵容，以预测那些最有可能产生准确识别的人，然后评估这些预测的准确性。如果验证研究证明是成功的，该项目的研究结果可用于开发新的现场工具，使阵容管理员能够更容易地实施有关填充物/可疑相似性的指导方针。更好地遵守这些准则可以提高指认指认的质量和准确性，从而减少错误定罪的发生率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.