Dr James Daniel Dunn | UNSW Research

My Expertise

Face Recognition and AI: Expert in the study of how humans and AI systems recognize and remember faces, with applications in security, policing, and justice.

Forensic Science: Specialist in improving the accuracy and fairness of forensic science practices, particularly in identity verification and criminal investigations.

Impact of Stress on Memory: Researcher focused on understanding how stress affects memory recall, especially in professions where accuracy is critical, such as law enforcement.

Keywords

cognitive psychology

face recognition

artificial intelligence

forensic sciences

machine learning

Fields of Research (FoR)

Forensic psychology, Sensory processes, perception and performance

Biography

Dr. James Dunn is an ARC DECRA Research Fellow and Lecturer in the School of Psychology at UNSW Sydney. His research focuses on face and person recognition, forensic science, and individual differences, using advanced methodologies such as behavioral methods, machine learning, AI and eye-tracking. Dr. Dunn's work bridges the gap between theoretical research and practical applications, particularly in high-stakes environments where accurate...view more

He is also a Pact for Impact School Champion (Psychology) and member of the Psychology Equity, Diversity & Inclusion team.

Research Interests

Face and Person Recognition: I study how we identify and remember faces and people, which is important for things like security and law enforcement. Faces tell us who people are and how they feel, and recognizing them is a complex skill that humans have evolved to do very well.
Improving Forensic Science: My work helps make forensic science more accurate and fair, which is crucial for solving crimes and ensuring justice. I collaborate with key industry and government partners, including the Australian Federal Police and NSW Police, to develop tools and strategies that enhance accuracy and fairness in areas like identity verification and criminal investigations.
Exploring Individual Differences: I look at why people perform differently on cognitive tasks, such as memory and attention, and what these differences mean in everyday life. This research is vital for understanding how stress impacts cognitive function, particularly in professions where memory accuracy can have significant consequences.
How Diverse Experiences Shape Face Recognition in Humans and AI: This project aims to understand how unique experiences contribute to expertise in face recognition using computational AI models. By exploring how different experiences affect our ability to recognize faces, we can develop AI that mimics these human skills. The expected outcomes include improved accuracy and fairness in face recognition, which is crucial for security, policing, and the justice system.

Broader Impact

Dr. Dunn's research has significant applied importance, especially in enhancing accuracy and fairness in identity verification, criminal investigations, and forensic science. He is also deeply invested in the implications of his findings for AI systems, aiming to reduce biases and improve the reliability of automated processes that mimic human cognitive functions. By addressing the challenges posed by individual variability in cognitive performance, his work provides essential insights for improving decision-making processes in sensitive domains.

My Grants

Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) - 2025-2028

Royal Society Te Apārangi Marsden Fund 2024-2027

Office of National Intelligence - National Intelligence Postdoctoral Grant (CI-A) - 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

UNSW Science PhD Writing Scholarship - 2018

Outstanding Research Student Award - 2017

UNSW Science Postgraduate Research Competition School of Psychology Prize - 2016

UNSW Science Postgraduate Research Competition Competition Winner - 2015

My Research Activities

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

Growns, B., Dunn, J. D., Helm, R. K., Towler, A., Mattijssen, E., & Martire, K. A. (2024). Jack of all trades, master of one: domain-specific and domain-general contributions to perceptual expertise in visual comparison. Cogn Res Princ Implic, 9(1), 73. https://doi.org/10.1186/s41235-024-00596-0

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

Tagliente, S., Passarelli, M., D’Elia, V., Palmisano, A., Dunn, J. D., Masini, M., Lanciano, T., Curci, A., & Rivolta, D. (2023). Self-reported face recognition abilities moderately predict face-learning skills: Evidence from Italian samples. Heliyon, 9(3). https://doi.org/10.1016/j.heliyon.2023.e14125

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

Growns, B., Dunn, J. D., Mattijssen, E., Quigley-McBride, A., & Towler, A. (2022). Match me if you can: Evidence for a domain-general visual comparison ability. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-021-02044-2

Growns, B., Dunn, J. D., Helm, R. K., Towler, A., & Kukucka, J. (2022). The low prevalence effect in fingerprint comparison amongst forensic science trainees and novices. PLoS One, 17(8), e0272338. https://doi.org/10.1371/journal.pone.0272338

Trinh, A., Dunn, J. D., & White, D. (2022). Verifying unfamiliar identities: Effects of processing name and face information in the same identity-matching task. Cogn Res Princ Implic, 7(1), 92. https://doi.org/10.1186/s41235-022-00441-2

Growns, B., Towler, A., Dunn, J. D., Salerno, J. M., Schweitzer, N. J., & Dror, I. E. (2022). Statistical feature training improves fingerprint-matching accuracy in novices and professional fingerprint examiners. Cogn Res Princ Implic, 7(1), 60. https://doi.org/10.1186/s41235-022-00413-6

Dunn, J. D., Kemp, R. I., & White, D. (2021). Top-down influences on working memory representations of faces: Evidence from dual-target visual search. Q J Exp Psychol (Hove), 74(8), 1368-1377. https://doi.org/10.1177/17470218211014357

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

Dunn, J. D., Ritchie, K. L., Kemp, R. I., & White, D. (2019). Familiarity does not inhibit image-specific encoding of faces. Journal of Experimental Psychology: Human Perception and Performance, 45(7), 841-854. doi:10.1037/xhp0000625

Towler, A, Kemp, R. I., Burton, A. M., Dunn, J.D., Wayne, T., Moreton, R., White, D. (2019). Do professional facial image comparison training courses work? PLoS One, 14(2), e0211037. https://doi.org/ 10.1371/journal.pone.0211037

Towler, A., Kemp, R. I., Bruce, V., Burton, A. M., Dunn, J. D., & White, D. (2019). Are face recognition abilities in humans and sheep really ‘comparable’? R. Soc. open sci., 6, 180772. doi:http://dx.doi.org/10.1098/rsos.180772

Dunn, J. D., Kemp, R. I., & White, D. (2018). Search templates that incorporate within-face variation improve visual search for faces. Cognitive Research: Principles and Implications, 3(37), 1-11. doi:10.1186/s41235-018-0128-1

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

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

Australia should punt on bold, unproven ideas: Shergold

Are you among Australia's best facial super-recognisers? Take our test to find out (theconversation.com)

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

PSYC1027 - Forensic Psychology: Crime, Courts and Corrections (Course Coordinator)

PSYC3301 - Psychology & Law (Course Coordinator)

PSYC2071 - Perception and Cognition (Lecturer)

PSYC1021 - Introduction to Psychological Applications (Lecturer)

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Location

Science

Mathews Building Room 1004

Map reference (Google map)

Contact

+61-2-9065-1425

j.d.dunn@unsw.edu.au

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Publications

by Dr James Daniel Dunn

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ORCID as entered in ROS

https://orcid.org/0000-0002-6964-909X

Videos

In this episode of Better Thinking, Nesh Nikolic speaks with Dr James Dunn about strategies that support superior face identification accuracy and contextual influences on face identification as well as his most interesting research on Super-recognisers.

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.