Role of motion in the recognition of familiar faces
My current research focuses on the importance of face animation in the recognition of familiar faces. Previous work has established that face movement aids the recognition of degraded famous faces. More recent work is investigating the impact of this finding, in applied contexts - for example, is the recognition of suspects from CCTV footage significantly more accurate (compared to the best still from the sequence) when moving footage is viewed? This information should be useful for police officers, when evaluating the usefulness of the captured CCTV images, for the accurate recognition of identity. I am also interested in the theoretical underpinings of the movement recognition advantage. Current models of familiar face recognition do not currently address why moving degraded faces are easier to recognise, compared to static face images. Investigation of this issue (for example, using priming techniques) should tell us more about the nature of the stored face representations, as well as how information is extracted and processed from the face.
Role of motion in the learning of unfamiliar faces
I am also interested in the role of motion when learning the identity of previously unfamiliar faces. Previous work (see Pike et al, 1997) has suggested that seeing faces moving rigidly (rotational motion of head & body) helps build robust face representations. Investigation of this issue allows us to determine the importance of face animation, both at learning and test, informing us how face representations may change and develop with familisation.
Individual variation in face recognition
Some people are good at face recognition and others are poor at it. I am interested in this individual variation and I have been conducting work looking at the relationship between face recognition and extraversion (Lander & Poyarkar, 2015). Ongoing work with Kiki Giannou is looking at the importance of compassion and empathy on face recognition ability. In addition I have worked with specific groups of participants who are very good (super-recognisers; with Josh Davis & Ashok Jansari) or very poor (developmental prosopagnosics; with Sarah Bate, Rachel Bennetts & Natalie Butcher) at face recognition.