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Face matching using a novel interactive procedure: Examining eye- and mouse-movement behaviour

   School of Social Sciences

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  Dr Harriet Smith, Dr Filipe Cristino, Prof T Baguley  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Checking photo ID is necessary for identity verification, enabling people to cross borders, buy age restricted goods, and access services. Incorrect decisions can have serious consequences. However, the task of deciding whether or not two images feature the same person is error-prone. We have developed a novel interactive procedure that improves face matching performance (Smith et al., 2021), enabling the user to interact with a facial image, fluidly moving it to different viewpoints to aid comparisons. However, little is known about the behaviour and relative contribution of cognitive mechanisms which account for the procedure’s performance advantage. The project will focus on this gap in the literature, identifying the characteristics of accurate interactive face matching behaviour to inform evidence-based training.  

We invite applicants to develop a proposal addressing these research aims using eye- and mouse-tracking methods. This could involve comparing standard (static) to interactive face matching performance across a range of practice-relevant conditions (e.g., face coverings, mismatch prevalence, facial similarity). Examining eye- and mouse-movement behaviour will provide fine-grained information about matching behaviour. The methodology will be state of the art, capitalising on recent advances in both eye and mouse-tracking. Experiments will be conducted both in the lab and online so that online data can be validated.  

This technical project offers the opportunity to develop a range of research skills including computer programming and the application of advanced, cutting-edge statistical techniques. The student will be supported by a supervision team specialising in identity perception (Harriet Smith), eye- and mouse-tracking (Filipe Cristino) and advanced statistics (Thom Baguley). They will also be encouraged to engage with the team’s external collaborators and relevant stakeholders.  

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