A Perceptual Scaling Approach to Eyewitness Identification

长期以来，目击者鉴定在刑事调查和起诉中发挥着重要作用，而查明罪犯对刑事司法程序至关重要。基于对感知和记忆的理解方面的进展，这个项目将研究“填充物”在序列中的作用。填充者是指在已知是无辜的阵容中，用来挑战目击者认可的个人。这项研究将开发一种基于人和机器面部相似度测量相结合的填充物选择的新方法。该项目建立了用于设计和实施目击者识别阵容的新范式。借鉴感觉心理学中公认的感知尺度，这种方法将被用来估计目击者记忆的强度，从而生成大量面部图像库，其中面部相似程度被量化。这项研究将有助于降低目击者对决策偏差的敏感性，并将提供感知到的面孔相似性的量化测量。这些措施与机器学习算法相结合，将被用于创建模型证人，这是一种重现人类观察者的相似性判断并预测新面孔的感知相似性的算法。该项目将产生一个客观的选择填充者的系统，以优化目击者的公平性和表现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Eyewitness identification has long played an important role in criminal investigations and prosecutions, where identifying the culprit is critical to the criminal justice process. Based on advances in understanding of perception and memory, this project will examine the role of “fillers” in lineups. Fillers are the individuals in a lineup known to be innocent who serve to challenge eyewitness recognition. The research will develop a novel method for filler selection based on a combination of human and machine measures of facial similarity.The project builds upon a new paradigm for the design and conduct of lineups for eyewitness identification. Drawing upon perceptual scaling, well-established in sensory psychology, this approach will be used to estimate the strength of eyewitness memories, and thus to generate large libraries of facial images in which the degree of facial similarity is quantified. This research will help to reduce susceptibility of the eyewitness to decision bias and will provide quantitative measures of the perceived similarity of faces. These measures, in combination with a machine learning algorithm, will be used to create a model witness, which is an algorithm that reproduces similarity judgements of human observers and predicts perceptual similarity for new faces. The project will yield an objective system for selecting fillers in order to optimize eyewitness fairness and performance.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.