Ritchie, Kay L., White, David, Kramer, Robin S.S. et al. (3 more authors) (2018) Enhancing CCTV:Averages improve face identification from poor-quality images. Applied Cognitive Psychology. pp. 671-680. ISSN 0888-4080
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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 |
Keywords: | averages,CCTV,face identification,pixelated images |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number ECONOMIC AND SOCIAL RESEARCH COUNCIL (ESRC) ES/J022950/2 EUROPEAN COMMISSION 20120411 |
Depositing User: | Pure (York) |
Date Deposited: | 11 Sep 2018 16:10 |
Last Modified: | 03 Nov 2024 01:22 |
Published Version: | https://doi.org/10.1002/acp.3449 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1002/acp.3449 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135571 |