A Perceptual Scaling Approach to Eyewitness Identification

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.