Understanding super-recognition to improve face identification systems

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Super-recognisers are people who consistently achieve the highest scores in challenging face identification tasks. The successful candidate will design a program of research to understand the visual information these individuals use when identifying faces, drawing on existing resources in the UNSW Face Research Lab. Resources include a large participant pool of Sydney-based super-recognisers, eye-tracking technology and a large dataset of challenging face identification decisions selected using face recognition algorithms. Studies will inform development of data-driven training and recruitment methods, which can improve face identification in security and legal settings, and the social functioning of people with impaired face identification ability.

In collaboration with the supervision team, the candidate will be expected to design a novel program of research to improve understanding of visual processing underpinning accurate face identification decisions. We hope to appoint a student who can convert this knowledge into innovative solutions to problems associated with poor face identification accuracy in professional settings.

The HDR will have proven ability to carry out independent research relative to opportunity. They will have an undergraduate degree in psychology and/or cognitive science and have demonstrated excellence in the research projects carried out in fulfilment of these degrees. With support from the supervision team and their network of collaborators, the student will help to develop scientific programming and eye-tracking protocols, and so experience of programming languages is an advantage. 

The HDR will have the ability to conduct research as part of a team in the service of common goals. Because this research is part of a broader project that aims to provide evidence-based solutions to improve face processing performed by humans and computers, experience working with end-users (e.g. government, police), industry and/or cross-disciplinary teams are also desirable.

Supervisory team