Data were analyzed from 39 research articles, involving 91 independent samples and nearly 5,000 participants.

The current article reviews the own-race bias (ORB) phenomenon in memory for human faces, the finding that own-race faces are better remembered when compared with memory for faces of another, less familiar race. Researchers reporting lineup fairness measures should specify the type of description presented, the amount of detail in the description, and whether the mock witnesses viewed target-present and/or -absent lineups. The reliability of lineup fairness measures across different descriptions was low and reliability across different sets of mock witnesses was moderate to high, depending on the measure. Correlations within lineup size measures and within some lineup bias measures indicated convergent validity and the correlations across the lineup size and lineup bias measures demonstrated discriminant validity. Target-absent lineups were consistently estimated to be fairer than target-present lineups-which is problematic because it suggests that lineups containing innocent suspects are less likely to be challenged in court than lineups containing guilty suspects. In comparison to descriptions compiled from multiple witnesses, when individual descriptions were presented in the mock-witness task, lineup fairness measures indicated a higher number of plausible lineup members but more bias toward the suspect. We conducted a large-scale, online experiment ( = 1,010) to examine how lineup fairness measures varied with mock-witness task methodologies as well as to explore the validity and reliability of the measures. Despite their importance, lineup fairness measures have received scant empirical attention and researchers inconsistently conduct and report mock-witness tasks and lineup fairness measures. Likewise, researchers often consider lineup fairness when comparing results across studies. Triers of fact sometimes consider lineup fairness when determining the suggestiveness of an identification procedure. This chapter, focuses on collaborative filtering algorithms for recommender systems, briefly explains how they work and what their limitations are. All current real-world large-scale recommender systems have invested on scalable distributed database systems for data storage and parallel and distributed algorithms for finding recommendations. The social information of an online social network can be useful both for improving content personalization but also for allowing existing algorithms to scale to huge datasets. In order to create a personalized user experience in social networks, we need data management solutions that scale well on the huge amounts of information generated on a daily basis. The result is a rich information repository which can be used to improve the user experience and increase their engagement if handled properly. It is typical among online social networks’ users to share their status, activity and other information with fellow users, to interact on the information shared by others and to express their trust or interest for each other. 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. 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. 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. In this paper, we describe our work towards using recommender systems for the photo lineup assembling task. 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. that the suspect differs significantly from all other candidates. One of the key factors affecting the incorrect identification of a suspect is the lack of lineup fairness, i.e. Unfortunately, there are many cases where lineups have led to the conviction of an innocent suspect.

This method is used to provide evidence in the prosecution and subsequent conviction of suspects. Photo lineups play a significant role in the eyewitness identification process.

Christmas photo police lineup