Towards Recommender Systems for Police Photo Lineup

TL;DR

This paper explores using recommender systems to improve the fairness and efficiency of photo lineups in eyewitness identification, proposing two methods based on visual and attribute similarities, with initial positive evaluations.

Contribution

It introduces two novel item-based recommendation methods for lineup assembly, combining visual and attribute-based approaches to enhance lineup fairness.

Findings

Visual descriptor-based recommendations performed better in initial tests.

Both recommendation methods are functional and produce diverse lineup suggestions.

Future work includes integrating methods and incorporating technician feedback.

Abstract

Photo lineups play a significant role in the eyewitness identification process. This method is used to provide evidence in the prosecution and subsequent conviction of suspects. Unfortunately, there are many cases where lineups have led to the conviction of an innocent suspect. One of the key factors affecting the incorrect identification of a suspect is the lack of lineup fairness, i.e. that the suspect differs significantly from all other candidates. Although the process of assembling fair lineup is both highly important and time-consuming, only a handful of tools are available to simplify the task. In this paper, we describe our work towards using recommender systems for the photo lineup assembling task. We propose and evaluate two complementary methods for item-based recommendation: one based on the visual descriptors of the deep neural network, the other based on the content-based attributes of persons. The initial evaluation made by forensic technicians shows that although results favored visual descriptors over attribute-based similarity, both approaches are functional and highly diverse in terms of recommended objects. Thus, future work should involve incorporating both approaches in a single prediction method, preference learning based on the feedback from forensic technicians and recommendation of assembled lineups instead of single candidates.