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Advanced robust probability regression for face recognition

Robustness in statistics can be defined as "the ability of an inferential statistic or procedure that is reliable, resistant, and avoidable. The pioneering work of robust statistics was primarily created by John Tukey and Peter Huber. Then the robust statistical theory is further developed by numerous articles.

Robust Inference has been used as a topic in data science and statistics for decades, with wide applications in financial econometrics, engineering, signal processing, computer version, Deep Learning, Machine Learning, and other fields. Robust regression analysis is becoming of paramount importance for addressing more general problems

This project will investigate new robust regression models and methods with application in face recognition.

Candidates with high MSc or BSc degree grades in Statistics, Financial Econometrics, Actuarial science, Mathematics and Technology are welcome to apply.

Read some references related to the topic:

  1. Linear regression for face recognition, IEEE Trans Pattern Anal Mach Intell 
  2. Robust Statistics,
  3. Robust Regression for Face Recognition 

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Keming Yu - Keming Yu – Chair in Statistics Research Director (Impact) – in Mathematical Sciences Keming joined Brunel University London in 2005. Before that he held posts at various institutions, including University of Plymouth, Lancaster University and the Open University. Keming got his first degree in Mathematics and MSc in Statistics from universities in China and got his PhD in Statistics from The Open University, Milton Keynes.

Hongying Meng - Dr Hongying Meng is a Reader with Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University London. Before that, he held research positions in several UK universities including University College London (UCL), University of YorkUniversity of Southampton, University of Lincoln, and University of Dundee. He received his Ph.D. degree in Communication and Electronic Systems from Xi’an Jiaotong University and was a lecturer in Electronic Engineering Department of Tsinghua University, Beijing in China. He is a Member of Engineering Professors’ Council, and a Fellow of The Higher Education Academy (HEA) in UK. He is a Senior Member of IEEE and an associate editor for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) and IEEE Transactions on Cognitive and Developmental Systems (IEEE TCDS), and a general chair for 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020).

Related Research Group(s)

Mathematical and Statistical Modelling

Mathematical and Statistical Modelling - Established in 2021 the Centre spotlights the transformative role of mathematics and statistics in innovative projects, their impact in real-world applications and in adding societal and economic value.