My Expertise
Face Recognition and AI: Study of how humans recognise and remember faces, and how these abilities compare to and can inform AI systems, with applications in security, policing, and digital identity.
AI-Generated Faces and Misinformation: Research on how people detect AI-generated images, particularly faces, and what this means for online safety, scams, and digital trust.
Memory and Stress: Investigation of how stress affects memory and recall, particularly in high-pressure environments such as law enforcement and emergency response.
Keywords
Fields of Research (FoR)
Forensic psychology, Sensory processes, perception and performanceBiography
Dr James Dunn is an ARC DECRA Research Fellow and Lecturer in the School of Psychology at UNSW Sydney. His research examines how people perceive, recognise, and remember information in real-world contexts, with a focus on face recognition, human–AI interaction, and memory under stress. Using behavioural experiments and computational approaches, his work connects fundamental cognitive science with applied challenges in policing, national...view more
Dr James Dunn is an ARC DECRA Research Fellow and Lecturer in the School of Psychology at UNSW Sydney. His research examines how people perceive, recognise, and remember information in real-world contexts, with a focus on face recognition, human–AI interaction, and memory under stress. Using behavioural experiments and computational approaches, his work connects fundamental cognitive science with applied challenges in policing, national security, and digital identity. He works closely with government and industry partners, including the Australian Federal Police, to translate research into practical tools and evidence-based solutions.
He is a member of the School of Psychology Equity, Diversity & Inclusion team.
Research Interests
- Face Recognition and Expertise: I study how people recognise faces and why some individuals are exceptionally good at it. This work helps identify and measure expertise, with applications in security, policing, and identity verification.
- Human–AI Interaction and AI-Generated Faces: My research explores how people detect AI-generated faces and how human perceptual expertise can inform the development of more reliable and trustworthy AI systems. This work addresses growing challenges around misinformation, scams, and digital identity.
- Memory and Decision-Making Under Stress: I investigate how stress and attention affect memory and decision-making in real-world contexts, particularly in high-pressure environments such as policing and emergency response.
- Individual Differences in Cognition: I examine why people vary in their perceptual and memory abilities, and how these differences can be understood, predicted, and applied in practical settings.
- Applied Cognitive Science and Translation: I develop tools, methods, and partnerships that translate research into practice, including personnel selection, training, and operational decision-making in government and industry.
Broader Impact
Dr Dunn’s research has direct real-world impact, improving how people and organisations make decisions about identity, memory, and information. His work has informed personnel selection and operational practices in policing and security, and contributes to public understanding of emerging issues such as AI-generated identities and misinformation. By combining insights from human cognition with technological innovation, his research supports safer, fairer, and more reliable systems in an increasingly complex and digital world.
My Grants
Australian Research Council (ARC) Discovery – The psychology of perceiving artificial people (2026–2030)
Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) – How do diverse experiences shape face recognition in humans and AI? (2025–2028)
Marsden Fund – Perceptual expertise of super-matchers and forensic science experts (2024–2027)
Office of National Intelligence – National Intelligence Postdoctoral Grant (CI-A) – Protecting police officers’ memories of critical incidents (2023–2025)
My Qualifications
BSc(Adv) (Psyc), UNSW Sydney, Sydney (2012)
Ph.D., UNSW Sydney, Sydney (2018)
My Awards
Early Career Impact Award - 2024
Community, Health & Safety, and Wellbeing Impact Award - 2023
UNSW Science Early Career Academic Award - 2021
UNSW Science ECAN Seeding Grant - 2020
My Research Activities
Research Highlights
- Dunn, J. D., White, D., Sutherland, C. A. M., Miller, E. J., Steward, B. A., & Dawel, A. (2026). Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it. Br J Psychol. https://doi.org/10.1111/bjop.70063
- Dunn, J. D., Miellet, S., & White, D. (2024). Information sampling differences supporting superior face identity processing ability. Psychon Bull Rev. https://doi.org/10.3758/s13423-024-02579-0
- Dunn, J. D., Towler, A., Popovic, B., de Courcey, A., Lee, N. Y., Kemp, R. I., Miellet, S., & White, D. (2024). Flexible use of facial features supports face identity processing. J Exp Psychol Hum Percept Perform. https://doi.org/10.1037/xhp0001242
- Dunn, J. D., Towler, A., Kemp, R. I., & White, D. (2023). Selecting police super-recognisers. PLoS One, 18(5), e0283682. https://doi.org/10.1371/journal.pone.0283682
- Towler, A., Dunn, J. D., Castro Martinez, S., Moreton, R., Eklof, F., Ruifrok, A., Kemp, R. I., & White, D. (2023). Diverse types of expertise in facial recognition. Sci Rep, 13(1), 11396. https://doi.org/10.1038/s41598-023-28632-x
- Dunn, J. D., Varela, V. P. L., Nicholls, V. I., Papinutto, M., White, D., & Miellet, S. (2022). Visual information sampling in super-recognizers. Psychological Science. 1-16. https://doi.org/10.1177/09567976221096320
- Dunn, J. D., Summersby, S., Towler, A., Davis, J. P., & White, D. (2020). UNSW Face Test: A screening tool for super-recognizers. PLoS One, 15(11), e0241747. https://doi.org/10.1371/journal.pone.0241747
- White, D., Dunn, J. D., Schmid, A. C., & Kemp, R. I. (2015). Error Rates in Users of Automatic Face Recognition Software. PLoS One, 10(10), e0139827. doi: 10.1371/journal.pone.0139827
My Research Supervision
Supervision keywords
Areas of supervision
Face and Person Recognition: Supervision in this area focuses on understanding how we identify and remember faces and people. This includes studying the cognitive processes involved and developing methods to improve accuracy in real-world applications such as security and law enforcement.
Forensic Science: This research area involves enhancing the accuracy and fairness of forensic science practices. Projects may include developing tools and strategies for identity verification and criminal investigations, in collaboration with industry and government partners like the Australian Federal Police and NSW Police.
Individual Differences in Cognitive Abilities: Supervision in this area explores why people perform differently on cognitive tasks such as memory and attention. Research projects may investigate the implications of these differences in everyday life and high-stakes environments.
Impact of Diverse Experiences on Face Recognition: Supervision in this area aims to understand how unique experiences contribute to expertise in face recognition. Research projects may use computational AI models to explore how different experiences affect face recognition accuracy and fairness, with applications in security, policing, and the justice system.
Currently supervising
Daniel Chu
My Engagement
Even experts can't tell if these faces are AI-generated or not. Can you?
AI study gives insights into why super-recognisers excel at identifying faces
Australia should punt on bold, unproven ideas: Shergold
Radio National Breakfast Show (AU) - Are you a super-recogniser?
Super-recognisers on "The Project" (Channel 10, AU; 24 Nov 2020)
Experts reveal how 'super recognisers' never forget a face
All of the Mind Podcast - Super-voice-recognisers
Take the UNSW Face Test to find out if you are a 'super-recogniser'.
My Teaching
PSYC3301 - Psychology & Law (Course Coordinator and Lecturer)
PSYC2071 - Perception and Cognition (Lecturer)
Publications
ORCID as entered in ROS
Videos
James Dunn is a Lecturer in the School of Psychology at UNSW Sydney. Current areas of interest include face and person recognition, forensic science and individual differences with both applied and theory-inspired research using behavioural methods, machine learning and eye-tracking.
Previous and current research projects: person-in-crowd identification, the strategies supporting superior face identification accuracy, and contextual influences on face identification.