Person identification and re-identification (Re-ID) has recently become a major topic in the field of computer vision and an essential component for the use of robotics in human-robot centric environments. Re-ID is a fundamental robotic capability for long-term interactions with people. It is important to know with whom the robot is interacting, as well as to remember user preferences. The problem is challenging due to a number of issues, not least due to the fact that person recognition systems often rely on visual full face views to enable face recognition but also encounter occlusions, background activity, viewpoint change, illumination conditions etc.
Many approaches to Re-ID are based on the use of images (rather than video) and focus on extracting discriminative features or metric learning. However, in reality, humans recognise each other from any viewpoint in any environment, often using soft biometrics as secondary information to improve the primary biometrics as they can be acquired from a distance. Soft biometrics can include personal attributes like gender, ethnicity, age, and physical characteristics that do not change significantly in adults over time. Hence, utilising soft biometric traits will improve person recognition accuracy.
Adversarial learning is a relatively new research field that lies at the intersection of machine learning and computer security. It aims at enabling the safe adoption of machine learning techniques in adversarial settings such as biometric recognition. Adversarial networks are designed where given a training set, they learn to generate new data with the same statistics as the training set. Based on an assumption that an image is composed of appearance and content factors, this approach removes the effects of changeable appearances.
This project will focus on innovations in computer vision and deep learning to develop novel analytical methods for Re-ID combining computer vision techniques for person identification with soft biometric features. Here, adversarial networks will be used to generate suitable training samples of soft biometric features to allow the system to generalise Re-ID to classes of persons with soft biometric features rather than individuals. The technology may be applied to many areas including human-robot collaboration in home, service and industrial environments. Using popular deep and shallow learning algorithms, we will train a mobile robotic system to readily identify humans in a dynamic human-robot centric environment. Benchmarking performance evaluation will be conducted using the well-known Market-1501, DukeMTMC-reID and CUHK03 datasets.
The Cognitive Robotics team in the Intelligent Systems Research Centre focuses on novel, advanced control methods for autonomous mobile robots, merging approaches from Artificial Intelligence, Cognitive Science and Engineering. Research in Cognitive Robotics at the ISRC ranges from investigating robotics as a science, to applications of robotics such as industrial robotics, assistive robotics and computer vision. The Cognitive Robotic laboratory contains a range of mobile robotics systems (Summit XLs, PR, Pioneers) equipped with 2D/3D vision sensors that can be utilised in this project.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
- A demonstrable interest in the research area associated with the studentship
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
- For VCRS Awards, Masters at 75%
- Publications - peer-reviewed
- Experience of presentation of research findings
- Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,500 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,750 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,500 (tbc) per annum for three years (subject to satisfactory academic performance). EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
I started my PhD at Ulster University after I received the master degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. My research interests are feature extraction, face verification and pattern recognition.The proudest moments of my PhD when my papers were accepted by journals and I received my PhD certificate. It is a long journey to pursue a PhD, I couldn't have got through this without the constant support, help and encouragement from my supervisors and friends. Many thanks all of them.
Huan Wan - PhD in Computer Science and Informatics
I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".
Xin Wei - PhD in Computer Science and Informatics
After master’s degree, I joined the Artificial Intelligence Research Group in the School of Computing at Ulster University to pursue my PhD. I would like to thank my supervisors for their guidance, invaluable advice, encouragement and support throughout my PhD.My proudest moments were when my research papers were accepted in prestigious conferences and journals. I feel accomplished about the six first-author publications from my doctoral research. Also, I have had the honour of receiving the Best Student Paper Award at the 2018 International FLINS Conference.I love travelling; my favourite memories were travelling to present my research in addition to getting the opportunity to meet leading researchers from different parts of the world. And I couldn't have achieved this without the support of my friends and family.
Niloofer Shanavas - PhD in Computer Sciences and Informatics
In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words
Jyotsna Talreja Wassan - PhD in Computer Science and Informatics