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Improving Face Matching Accuracy Using Human Machine Teams

  • Full or part time
  • Application Deadline
    Friday, April 03, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

The aim of this project is to explore how human operators making face matching decisions can be optimally assisted by state of the art face matching algorithms, ultimately helping people to make quicker, more effective and more accurate decisions. These explorations will be used to design an integrated human-machine team for face matching that optimises accuracy in face matching tasks.

Face matching is a surprisingly error prone task with substantial variation in individual human performance. However, face matching by human observers is widely used to verify identity in applied, professional settings, where the ramifications of an error can be severe and potentially life changing. Recently there have been major gains in the accuracy of automated facial recognition algorithms using machine learning and artificial intelligence.

Laboratory research using constrained scenarios have demonstrated that fusing algorithm scores with the ratings of top human performance can provide almost perfect accuracy on a challenging face matching task. To obtain such high levels of performance in more natural scenarios will require research to understand how best to form human-machine teams for this task. We propose a series of experiments that will first use established tools to obtain human and machine baseline results. From this we will investigate novel ways to combine human and machine intelligence to improve facial matching decisions. Of particular interest will be investigating the concept of calibrating trust between human and machine to facilitate optimal human-machine team performance. The findings from these first two stages of research can subsequently be used to inform best practice in designing processes that minimise the risk of errors and misidentification in applied settings.

This project will combine the talents of psychologists at Glasgow University and applied researchers and technologists at Qumodo to provide a unique PhD training opportunity.

Funding Notes


Eligibility:
Applicants must meet the following eligibility criteria:
• A good first degree (at least 2:1) in psychology or related field
• Demonstrate an interest in topics such as visual perception, human-computer interaction, cognition, computer programming, machine learning, data science or social cognition
• Have a good grounding in statistics, experimental psychology and the collection and analysis of data

Applicants must complete the Supervisor Led Awards Eligibility Checker before proceeding with their application: https://glasgow.onlinesurveys.ac.uk/supervisor-led-awards-esrc-award-eligibility-checker-202
Students must meet ESRC eligibility criteria. ESRC eligibility information can be found here*: https://esrc.ukri.org/skills-and-careers/doctoral-training/prospective-students/

Award Details:
The scholarship is available as a +3 or a 1+3 programme depending on prior research training. This will be assessed as part of the recruitment process. The programme will commence in October 2020. It includes:
• an annual maintenance grant at the RCUK rate
• fees at the standard Home rate
• students can also draw on a pooled Research Training Support Grant, usually up to a maximum of £750 per year

http://www.sgsss.ac.uk/studentship/improving-face-matching/

Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 10 April, 2020. Interviews will take place on 17 April, 2020.

All scholarship awards are subject to candidates successfully securing admission to the PhD programme within the School of Psychology at University of Glasgow. Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.

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