Portrait of Dr Matthew Fysh

Dr Matthew Fysh

Research Associate

About

Matt studied for his BSc, MSc, and PhD in Psychology at the University of Kent. Upon completing his PhD in 2017, he joined the School of Psychology as a Lecturer, and then in 2018 joined the ‘CogSoCoAGE’ research team as a postdoctoral researcher.

Currently, Matt is working as a postdoctoral research associate on a two-and-a-half-year ESRC-funded project with Dr Markus Bindemann that investigates person identification in realistic contexts that will be rendered in virtual reality.

Research interests

Matt's research interests encompass the cognitive processes surrounding the identification of unfamiliar faces. The focus of his PhD research was on forensic face matching, which entails a comparison between two unfamiliar faces, to decide whether they depict the same identity or two different identities. This task is relevant to a number of real-world settings (most notably Passport Control), and is therefore rather applied in its nature.

At the same time, he is also exploring questions that speak to the more theoretical nature of this task, such as why observers might be more likely to classify two faces as the same person as opposed to different people, and how such biases might be averted or alleviated.

Matt is currently working as a postdoctoral researcher on an ESCR-funded project awarded to Dr Markus Bindemann that investigates person identification within virtual reality.

Key publications

  • Bindemann, M., Fysh, M. C., Sage, S., & Tummon, H. (2017). Person identification from aerial footage by a remote-controlled drone. Nature Scientific Reports, 7, 13629. https://doi.org/10.1038/s41598-017-14026-3
  • Fysh, M. C., & Bindemann, M. (2018). Human-computer interaction in face matching. Cognitive Science, 42, 1714-1732. https://doi.org/10.1111/cogs.12633
  • Fysh, M. C., & Bindemann, M. (2018). The Kent Face Matching Test. British Journal of Psychology, 109, 219-231. https://doi.org/10.1111/bjop.12260
  • Fysh, M. C., & Bindemann, M. (2017). Forensic face matching: A review. In M. Bindemann & A. M. Megreya (Eds.), Face Processing: Systems, Disorders, and Cultural Differences (1-20). New York, NY: Nova Science Publishers Inc.

The Kent Face Matching Test

Matt also developed the Kent Face Matching Test (KFMT), which is available as a research tool for researchers to download at https://www.kent.ac.uk/school-of-psychology/kentfacematch/index.html

Its corresponding paper can be found at https://onlinelibrary.wiley.com/doi/full/10.1111/bjop.12260

Teaching


Professional

Article

  • Robertson, D. J., Fysh, M. C., & Bindemann, M. (2019). Face identity verification: Five challenges facing practitioners. The Keesing Journal of Documents and Identity, 59, 3-8.
  • Fysh, M. C., & Bindemann, M. (2018). Person identification from drones by humans: Insights from Cognitive Psychology. Drones, 2, 32. doi: 10.3390/drones2040032
  • Fysh, M. C., & Bindemann, M. (2018). Human-computer interaction in face matching. Cognitive Science, 42, doi:10.1111/cogs.12633
  • Fysh, M. C., & Bindemann, M. (2018). The Kent Face Matching Test. British Journal of Psychology, 109, 219-231. doi:10.1111/bjop.12260
  • Fysh, M. C. (2018). Individual differences in the detection, matching and memory of faces. Cognitive Research: Principles and Implications, 3:20. doi:10.1186/s41235-018-0111-x
  • Bindemann, M., Fysh, M. C., Sage, S., Douglas, K., & Tummon, H. (2017). Person identification from aerial footage by a remote-controlled drone. Scientific Reports, 7, 1-10. doi:10.1038/s41598-017-14026-3
  • Fysh, M. C., & Bindemann, M. (2017). Effects of time pressure and time passage on face-matching accuracy. Royal Society Open Science, 4, 1-13. doi: 10.1098/rsos.170249
  • Bindemann, M., Fysh, M., Cross, K., & Watts, R. (2016). Matching faces against the clock. i-Perception, 7, 2041669516672219. doi: 10.1177/2041669516672219
  • Alenezi, H. M., Bindemann, M., Fysh, M. C., & Johnston, R. A. (2015). Face matching in a long task: Enforced rest and desk-switching cannot maintain identification accuracy. PeerJ, 3, e1184. doi:10.7717/peerj.1184

Book section

  • Fysh, M. C., & Bindemann, M. (2017). Forensic face matching: A review. In M. Bindemann, & A. M. Megreya (Eds.), Face Processing: Systems, Disorders, and Cultural Differences. New York, NY: Nova Science Publishing, Inc.

Conference presentations

  • Fysh, M. C., & Bindemann, M. (2018). Human-computer interaction in face matching. Presented at the International Congress on Applied Psychology (25-30 June, 2018), held in Montreal, Canada.
  • Fysh, M. C., & Bindemann, M. (2017). Human-computer interaction in face matching. Poster presented at Face Recognition at Its Best (19-20 September, 2017), held in London, UK.
  • Fysh, M. C., & Bindemann, M. (2017). Human-computer interaction in face matching. Poster presented at European Conference on Visual Perception (27-31 August, 2017), held in Berlin, Germany.

Publications

Article

  • Fysh, M., & Bindemann, M. (2018). Person Identification from Drones by Humans: Insights from Cognitive Psychology. Drones, 2, 1-11. doi:10.3390/drones2040032
    The deployment of unmanned aerial vehicles (i.e., drones) in military and police operations implies that drones can provide footage that is of sufficient quality to enable the recognition of strategic targets, criminal suspects, and missing persons. On the contrary, evidence from Cognitive Psychology suggests that such identity judgements by humans are already difficult under ideal conditions, and are even more challenging with drone surveillance footage. In this review, we outline the psychological literature on person identification for readers who are interested in the real-world application of drones. We specifically focus on factors that are likely to affect identification performance from drone-recorded footage, such as image quality, and additional person-related information from the body and gait. Based on this work, we suggest that person identification from drones is likely to be very challenging indeed, and that performance in laboratory settings is still very likely to underestimate the difficulty of this task in real-world settings.
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