By the same authors

From the same journal

Enhancing CCTV: Averages improve face identification from poor-quality images

Research output: Contribution to journalArticle

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalApplied Cognitive Psychology
DateAccepted/In press - 27 Jul 2018
DateE-pub ahead of print - 17 Aug 2018
DatePublished (current) - 1 Nov 2018
Issue number6
Volume32
Number of pages10
Pages (from-to)671-680
Early online date17/08/18
Original languageEnglish

Abstract

Low-quality images are problematic for face identification, for example, when the police identify faces from CCTV images. Here, we test whether face averages, comprising multiple poor-quality images, can improve both human and computer recognition. We created averages from multiple pixelated or nonpixelated images and compared accuracy using these images and exemplars. To provide a broad assessment of the potential benefits of this method, we tested human observers (n = 88; Experiment 1), and also computer recognition, using a smartphone application (Experiment 2) and a commercial one-to-many face recognition system used in forensic settings (Experiment 3). The third experiment used large image databases of 900 ambient images and 7,980 passport images. In all three experiments, we found a substantial increase in performance by averaging multiple pixelated images of a person's face. These results have implications for forensic settings in which faces are identified from poor-quality images, such as CCTV.

Bibliographical note

© 2018 John Wiley & Sons, Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

    Research areas

  • averages, CCTV, face identification, pixelated images

Projects

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations