Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Humans and algorithms for facial recognition: the effects of candidate list length and experience on performance|
|Citation:||Journal of Applied Research in Memory and Cognition, 2018; 7(4):597-609|
|Rebecca Heyer, Carolyn Semmler, Andrew T. Hendrickson|
|Abstract:||These experiments investigated how the candidate list displayed to facial reviewers affects their performance in a one-to-many unfamiliar face matching task. Automated facial recognition systems present the results of a database search and require selection of an image that matches the target. Few studies investigate how humans in combination with facial recognition algorithms perform within different operational contexts. These experiments investigated how the candidate list displayed to facial reviewers affects their performance in a one-to-many unfamiliar face matching task. We tested candidate list length with inexperienced (Experiment 1) and experienced (Experiment 2) facial reviewers. Candidate list length had a large impact on performance, varying with the operational context. However, response-time analyses show that the accurate responses were resolved quickly, with an error-prone guess process implemented after failed search. Long candidate lists (100 images) produced more false alarms, fewer hits, lower decision confidence, and increased response latencies among both inexperienced and experienced facial reviewers.|
|Keywords:||Unfamiliar face matching; facial recognition systems|
|Rights:||Crown Copyright © 2018 Published by Elsevier Inc. on behalf of Society for Applied Research in Memory and Cognition. All rights reserved.|
|Appears in Collections:||Aurora harvest 4|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.