Our research group moved to the University of York in January 2013. For more information on our work please see our webpage www.facevar.com National security officials, officers in the criminal justice system and retail staff frequently rely on face recognition to establish and authenticate the identity of an individual. At UK Border Control, officials work to ensure that only those passengers whose passport photo matches their face are allowed to enter the country. In the criminal justice system, police officers often utilise CCTV images as a means of identifying the perpetrator of a crime. In addition, cashiers in retail stores must examine face-photo ID cards in order to prohibit the illegal sale of age-restricted goods. Each of these occupations relies on the ability to detect correctly whether or not the face of an unfamiliar person matches a face photo on an ID card or an image still. Our reliance on face recognition for identity verification may stem from the fact that in some instances we show a high level of expertise in this area. For example, we are able to recognise familiar faces across a large range of highly variable photos, apparently effortlessly. However, in a striking contrast, recent research has shown that we are surprisingly poor at recognising new instances of an unfamiliar person. This distinction has major implications for applied professions in which accurate unfamiliar face recognition is vital. My work focuses on improving our understanding of human and machine-based face recognition, and how we might improve unfamiliar face recognition in real-world professions.

National security officials, officers in the criminal justice system and retail staff frequently rely on face recognition to establish and authenticate the identity of an individual. At UK Border Control, officials work to ensure that only those passengers whose passport photo matches their face are allowed to enter the country. In the criminal justice system, police officers often utilise CCTV images as a means of identifying the perpetrator of a crime. In addition, cashiers in retail stores must examine face-photo ID cards in order to prohibit the illegal sale of age-restricted goods. Each of these occupations relies on the ability to detect correctly whether or not the face of an unfamiliar person matches a face photo on an ID card or an image still. Our reliance on face recognition for identity verification may stem from the fact that in some instances we show a high level of expertise in this area. For example, we are able to recognise familiar faces across a large range of highly variable photos, apparently effortlessly. However, in a striking contrast, recent research has shown that we are surprisingly poor at recognising new instances of an unfamiliar person. This distinction has major implications for applied professions in which accurate unfamiliar face recognition is vital. My work at Aberdeen focused on improving our understanding of human and machine-based face recognition, and how we might improve unfamiliar face recognition in real-world professions.

I completed my PhD in Cognitive Neuroscience at the UCL Institute of Cognitive Neuroscience. I was supervised by Professor Nilli Lave and my research focused on applications of load theory in both clinical and neuro-typical domains. In particular, I assessed the efficacy of using perceptual load to reduce distraction in ADHD and I also investigated the effect of processing load on the perception of time. I graduated from the ICN in May 2014.

I completed an M.A.(Honours) degree in Psychology at the University of Glasgow, graduating in June 2009.