This project represents a new way to look at the problem of human face recognition. Despite a large amount of research on this topic, we still do not understand the most fundamental aspect of face processing: how can we identify the people we see? This is a key problem in human perception, but it also has practical implications in forensic and security settings. This project has its roots in a simple observation: pictures of the same face can look very different indeed. In the standard approach to face recognition, this commonplace fact is treated as an inconvenience. Differences between pictures of the same person are regarded as 'noise', and either ignored or eliminated by systematically controlling the images used for research. This research programme takes exactly the converse approach. Instead of trying to control away this variability, it will be studied explicitly. Under this approach, the problem of face recognition is not how to 'tell people apart', but instead how to 'tell people together' - how to bring together superficially different images into a coherent representation. Early work suggests that a very important component of familiar face recognition is the ability to generalize over superficial image differences - differences which tend to fool unfamiliar viewers, as well as automatic computer-based systems. The current failure to address this variability may account for the slow progress in face identification - progress which has fallen behind the understanding of other aspects of face processing such as social perception. This research comprises three components. First, a systematic examination will be conducted of the physical differences between images of the same person. Applying statistical techniques to graphical data, the aim is to specify what aspects of face images vary commonly, and what aspects vary idiosyncratically to that person. Second, a series of behavioural experiments will examine the nature of our representations of familiar faces - the hypothesis is that this representation needs to incorporate variability. Third, a series of studies will address practical face recognition by human observers (e.g. for security purposes). Computer-based approaches will also be examined in this strand - as these systems remain very poor, despite the claims of vendors. This novel approach to face identification has the potential to make a significant contribution to an area which has progressed rather slowly in recent years.

Many people with chronic pain use prescribed medicines. It is important that the correct medicines are prescribed to ensure effective pain management. For some people, changing the medicines they take might improve their pain management. We believe that regular review of medication could be beneficial for patients with chronic pain. Pharmacists have the knowledge and training to review patients’ medicines and identify changes that could lead to better pain control. Until recently, changes to prescriptions recommended by a pharmacist had to be made by a doctor. However many pharmacists are now registered to write prescriptions. We want to assess the benefits of pharmacist review of pain medicines and prescribing. We will compare three types of care: 1) pharmacist medicine review (using patient notes) and pharmacist prescribing, with face-to-face patient consultations; 2) pharmacist medicine review (using patient notes) with recommendations to GP; 3) standard care. Patients with chronic pain from six GP practices will be identified and divided into three groups. Practice pharmacists will review pain medication for groups 1) and 2) and compare this with best practice, recommending changes if necessary. Afterwards we will compare the groups for any differences in the way people feel their pain is affecting them.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

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