  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.
