Professor Brian Lovell
Professor

Brian Lovell

Email: 
Phone: 
+61 7 336 54134

Background

Brian C. Lovell, born in Brisbane, Australia in 1960, received his BE in Electrical Engineering (Honours I) in 1982, BSc in Computer Science in 1983, and PhD in Signal Processing in 1991, all from the University of Queensland (UQ). Currently, he is the Project Leader of the Advanced Surveillance Group at UQ. Professor Lovell served as the President of the International Association of Pattern Recognition from 2008 to 2010, is a Senior Member of the IEEE, a Fellow of the IEAust, Fellow of the Asia-Pacific AI Association, and has been a voting member for Australia on the Governing Board of the International Association for Pattern Recognition since 1998.

He is an Honorary Professor at IIT Guwahati, India; an Associate Editor of the Pattern Recognition Journal; an Associate Editor-in-Chief of the Machine Learning Research Journal; a member of the IAPR TC4 on Biometrics; and a member of the Awards Committee and Education Committee of the IEEE Biometrics Council.

In addition, Professor Lovell has chaired and co-chaired numerous international conferences in the field of pattern recognition, including ICPR2008, ACPR2011, ICIP2013, ICPR2016, and ICPR2020. His Advanced Surveillance Group has collaborated with port, rail, and airport organizations, as well as several national and international agencies, to develop technology-based solutions for operational and security concerns.

His current research projects are in the fields of:

  • Artificial Intelligence
  • StyleGAN
  • Stable Diffusion
  • Deep Learning
  • Biometrics
  • Robust Face Recognition using Deep Learning
  • Masked Face Recognition for COVID-19 Pandemic
  • Adversarial Attacks on AI Systems
  • Digital Pathology
  • Neurofibroma Detection and Assessment
  • Object Detection with Deep Learning

I am actively recruiting PhD students in Artificial Intelligence to work with my team. If you are interested and have a strong record from a good university, with a publication in a good conference such as CVPR, ICCV, ECCV, or MICCAI please send your CV to me. Full Scholarships (Tuition and Living) can be awarded within one month for truly exceptional candidates.

Availability

Professor Brian Lovell is:
Available for supervision
Media expert

Fields of research

Artificial intelligence Computer vision Computer vision and multimedia computation Engineering Image processing Information and Computing Sciences Pattern recognition

Qualifications

  • Bachelor (Honours) of Engineering, The University of Queensland
  • Bachelor of Science, The University of Queensland
  • Doctor of Philosophy, The University of Queensland

Research interests

  • Face Recognition with Deep Learning

    We develop new technologies to improve face recognition. Our group is first in the world to develop face recognition databases based entirely on synthetic faces. Other aspects of face recognition and affective computing (determining emotions from facial expressions) are current research themes within the group.

  • Object Detection Using Deep Learning

    We are researching improved techniques to identify small objects with high precision

  • Synthetic Face and Image Generation

    We were the first to investigate training face recognition systems on synthetic faces.

Research impacts

I have been pleased that my biometrics and other research has and is being been adopted commercially worldwide. My earlier face recognition systems have been installed by the University of San Francisco and Swinburne University among many other sites. More recently we have developed face recognition systems that are insensitive to the wearing of masks. These systems depend on our EDITH Ethical Face database of synthetic faces. To the best of our knowledge, we are the only group worldwide who can synthesise faces to order to train advanced ethical face recognition systems.

These systems have been adopted in the UK in 2020 by Facewatch Ltd and are currently being considered by the UK National Health Service and also Queensland Health to manage COVID 19 quarantine facilities and border control. In 2020-2021 we developed a touchless face mask fitting system for health workers to reduce the wastage of PPE and improve COVID19 management. This system is deployed on Queensland Health IT infrastructure in February 2021 and is planned to be made available nationally and internationally. The system has the potential to save millions of dollars in wasted PPE.

PRIZES, HONOURS AND AWARDS

Fellow of the IAPR, 2008 Multiple Best Paper prizes. Awarded Certificate of Recognition as most downloaded author at UQ by UQCybrary. Over 26,000 copies of my research papers were downloaded from the UQ EPrints archive in the 12 months ending May, 2005. APICTA Trophy for Best Research and Development, 2011, Face Recognition in a Crowd IFSEC Trophy 2011, Best CCTV Product of the Year (excluding cameras and lens), Face Recognition in a Crowd Technology Winner, ADS Security Innovation Award, 2021, Galahad facial detection and recognition software, awarded by the UK Home Office at the Security and Policing Show on March 9, 2021.

Search Professor Brian Lovell’s works on UQ eSpace

352 works between 1988 and 2024
All (352) Journal Article (101) Book Chapter (19) Conference Publication (222) Book (1) Other Outputs (3) Edited Outputs (6)

341 - 352 of 352 works

1993

Journal Article

The Relationship Between Instantaneous Frequency and Time-Frequency Representations

Lovell, Brian C., Williamson, Robert C. and Boashash, Boulem (1993). The Relationship Between Instantaneous Frequency and Time-Frequency Representations. IEEE Trans. Signal Processing, 41 (3), 1458-1461. doi: 10.1109/78.205756

The Relationship Between Instantaneous Frequency and Time-Frequency Representations

1993

Conference Publication

Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images

Paget, R., Lovell, Brian C. and Longstaff, I. D. (1993). Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images. Digital Image Computing: Techniques and Applications, Sydney, Australia, December, 1993.

Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images

1992

Journal Article

A Unified Approach to the Stft, Tfds, and Instantaneous Frequency

Kootsookos, PJ, Lovell, BC and Boashash, B (1992). A Unified Approach to the Stft, Tfds, and Instantaneous Frequency. Ieee Transactions On Signal Processing, 40 (8), 1971-1982. doi: 10.1109/78.149998

A Unified Approach to the Stft, Tfds, and Instantaneous Frequency

1992

Journal Article

The Statistical Performance of Some Instantaneous Frequency Estimators

Lovell, Brian C. and Williamson, R. C. (1992). The Statistical Performance of Some Instantaneous Frequency Estimators. IEEE Transactions on Signal Processing, 40 (7), 1708-1723. doi: 10.1109/78.143443

The Statistical Performance of Some Instantaneous Frequency Estimators

1992

Journal Article

A Unified Approach to the STFT, TFDs and Instantaneous Frequency

Kootsookos, Peter J., Lovell, Brian C. and Boashash, B. (1992). A Unified Approach to the STFT, TFDs and Instantaneous Frequency. IEEE Trans. Signal Processing, 40 (8), 1971-1982.

A Unified Approach to the STFT, TFDs and Instantaneous Frequency

1992

Conference Publication

Speech Enhancement for Robust Speaker Verification

Kootsookos, Peter J., Tsoi, A. C. and Lovell, Brian C. (1992). Speech Enhancement for Robust Speaker Verification. Speech, Science and Technology conference, Brisbane, December.

Speech Enhancement for Robust Speaker Verification

1991

Conference Publication

The Circular Nature Of Discrete-Time Frequency Estimates

Lovell, Brian C., Kootsookos, Peter J. and Williamson, R. C. (1991). The Circular Nature Of Discrete-Time Frequency Estimates. IEEE International Conference on ASSP, Toronto, May, 1991. Publ by IEEE. doi: 10.1109/icassp.1991.150176

The Circular Nature Of Discrete-Time Frequency Estimates

1991

Other Outputs

Techniques for non-stationary spectral analysis

Lovell, Brian Carrington (1991). Techniques for non-stationary spectral analysis. PhD Thesis, School of Computer Science and Electrical Engineering. doi: 10.14264/uql.2015.228

Techniques for non-stationary spectral analysis

1990

Conference Publication

Efficient Frequency Estimation and Time-Frequency Representations

Lovell, Brian C., Kootsookos, Peter J. and Williamson, Robert C. (1990). Efficient Frequency Estimation and Time-Frequency Representations. International Symposium on Signal Processing and Its Applications 1990, Gold Coast, Australia, August.

Efficient Frequency Estimation and Time-Frequency Representations

1989

Conference Publication

Time-Frequency Signal Analysis and Instantaneous Frequency Estimation: Methodology, Relationships and Implementation

Boashash, Boualem, Lovell, Brian C. and Kootsookos, Peter J. (1989). Time-Frequency Signal Analysis and Instantaneous Frequency Estimation: Methodology, Relationships and Implementation. International Symposium on Circuits and Systems, 1989. doi: 10.1109/iscas.1989.100579

Time-Frequency Signal Analysis and Instantaneous Frequency Estimation: Methodology, Relationships and Implementation

1988

Journal Article

Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution

Boashash B., Lovell B. and White L. (1988). Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution. Proceedings of SPIE - The International Society for Optical Engineering, 826, 104-114. doi: 10.1117/12.942021

Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution

1988

Conference Publication

Segmentation of Non-Stationary Signals with Applications

Boashash, B. and Lovell, Brian C. (1988). Segmentation of Non-Stationary Signals with Applications. IEEE International Conference on ASSP, New York, 1988. doi: 10.1109/icassp.1988.197203

Segmentation of Non-Stationary Signals with Applications

Current funding

  • 2024 - 2029
    Application of AI/Machine Learning, computer vision and automated systems
    UniQuest Pty Ltd
    Open grant
  • 2024 - 2025
    The Neurofibromatosis type 1 (NF1) Cutaneous Neurofibroma Consortium: Identifying Genetic modifiers of disease burden to inform treatment pathways (MRFF Neurofibromatosis led by Uni Newcastle)
    University of Newcastle
    Open grant
  • 2021 - 2025
    UQAI Scholarship
    AR Live Systems Ltd
    Open grant

Past funding

  • 2020 - 2021
    N95 Mask Fitment
    Queensland Health
    Open grant
  • 2019 - 2021
    AR Live Face Recognition and AI Project
    AR Live Systems Ltd
    Open grant
  • 2019 - 2021
    Justified Autonomous Unmanned Aerial System Effect (Defence CRC for Trusted Autonomous Systems project led by Skyborne Technologies Pty Ltd)
    Skyborne Technologies Pty Ltd
    Open grant
  • 2019
    Development of a standalone program for the automation of quantitative fractography - 2
    Commonwealth Defence Science and Technology Group
    Open grant
  • 2019
    Expanding Wiener, a high performance GPU cluster
    UQ Research Facilities Infrastructure Grants
    Open grant
  • 2018 - 2020
    Digitisation and image recognition in environmental chemistry
    UniQuest Pty Ltd
    Open grant
  • 2017 - 2022
    Fusion of Digital Microscopy and Plain Text Reports for Automated Analysis
    ARC Linkage Projects
    Open grant
  • 2017 - 2018
    Further development of a demonstrator for the automation of quantitative fractography
    Commonwealth Defence Science and Technology Group
    Open grant
  • 2017 - 2019
    Vision based automated corrosion analysis for galvanised steel lattice towers
    UniQuest Pty Ltd
    Open grant
  • 2016 - 2017
    Development of a demonstrator for the automation of quantitative fractography
    Commonwealth Defence Science and Technology Group
    Open grant
  • 2015
    ILC Coal Carry Back Project
    Australian Mathematical Sciences Institute Industry Internship Program
    Open grant
  • 2014 - 2015
    AMSI computer vision project
    Australian Mathematical Sciences Institute Industry Internship Program
    Open grant
  • 2013 - 2017
    Application of manifold-based image analysis to identify subtle changes in digitally-captured pathology samples
    ARC Linkage Projects
    Open grant
  • 2013
    AMSI Internship Program - Vehicle number plate identification
    Australian Mathematical Sciences Institute Industry Internship Program
    Open grant
  • 2013 - 2014
    Investigating repeatable ionospheric features during large space storms and superstorms
    United States Asian Office of Aerospace Research and Development
    Open grant
  • 2012 - 2016
    Forensic reasoning and uncertainty: Identifying pattern and impression expertise
    ARC Linkage Projects
    Open grant
  • 2011 - 2013
    Baseline Rail Level Crossing Video (R2.119)
    CRC for Rail Innovation
    Open grant
  • 2010 - 2012
    Assessing error in forensic identification: The development of scientific and legal standards of evidence
    UQ Collaboration and Industry Engagement Fund
    Open grant
  • 2007 - 2009
    Markov field theory applied to sensor networks analysis and design (ARC DP0772218 administered by University of South Australia)
    University of South Australia
    Open grant
  • 2004
    ARC Network in Imaging Science and Technology
    ARC Seed Funding for Research Networks
    Open grant
  • 1996
    Development of metrics for texture classification algorithms
    University of Queensland New Staff Research Grant
    Open grant

Availability

Professor Brian Lovell is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • Detecting and Classifying Neurofibromas using Deep Learning

    Neurofibromatosis type 1 (NF1) is one of the most common single-gene inherited disorders globally, with an incidence of 1/2500 individuals. While several phenotypes are associated with the disorder, the most common manifestation is cutaneous neurofibroma. The majority of adults develop these distressing cutaneous tumours (cNF), which increase in severity with age. Adult patients report cosmetic disfigurement due to cNF as the greatest burden of living with NF1. There is no way to predict tumour severity which can range from <100 to thousands. Youth and families experience reduced quality of life due to concerns about this uncertain future. We don’t yet understand why this condition is so variable or have any effective medical treatments. In the proposed research, we will assemble a consortium of internationally recognised experts in NF1 with access and capacity to recruit and phenotype patients to drive the largest genome-wide association and epigenome-wide association studies of the modifier gene networks driving the cutaneous phenotypic variance in NF1. We will then use individualised pharmacological annotation of these networks to identify precision treatment options to mitigate the most distressing and life quality damaging aspects of this devastating illness.

  • Classifying Gram Stain Images Using Transformers and Deep Learning

    Microscopic diagnosis of Gram stain smears is one of the most time and labor intensive tasks in the clinical setting. With the recent development of automated digital pathology scanners, it is now possible to economically obtain high-resolution Gram stain whole slide images for routine diagnosis. This finally opens the doorway to automated identification of bacteria types from digital images in a clinical setting. However, Gram stain whole slide images comprise billions of pixels and suffer from high morphological heterogeneity as well as from many different types of artifacts. Identifying multiple types of tiny bacteria with various densities from an extremely large whole slide image is incredibly challenging. To this end, we propose an end-to-end framework with a novel loss function that tackles the patch aggregation while considering the correlation of different labels in this multi-label scenario. Our framework first effectively integrates the relations among multiple patch features, and then utilizes a class aggregator to generate a robust slide-level feature representation under multi-label setting. Furthermore, we propose a novel loss function integrating two regularization terms: 1) a negative regulator that reduces the confusion between bacteria and negative samples without any bacteria, and 2) an adversarial loss that alleviates the impact of background specification among various tissue samples. We show that the proposed method achieves superior performance compared to several state-of-the-art methods.

  • Incremental Learning for AI

    Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task — a problem known as catastrophic forgetting. We address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO.

  • Text to Face Synthesis using Stable Diffusion for Biometrics Research

    Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual description of faces can be much more complicated and detailed due to the variety of facial attributes and the parsing of high dimensional abstract natural language. We propose a text-to-face model that should not only produce images in high resolution (10241024) and text-to-image consistency, but also output multiple faces to cover a wide range of unspecified facial features in a natural way. By fine-tuning the multi-label classifier and image encoder, our model obtains the vectors and image embeddings which are used to manipulate the noise vector sampled from the normal distribution. Afterwards, the manipulated noise vector is fed into a pre-trained high-resolution image generator to produce a set of faces with desired facial attributes. We refer to our model as TTF-HD. Experimental results show that TTF-HD generates high-quality faces and achieves state-of-the-art performance.

Supervision history

Current supervision

  • Doctor Philosophy

    Medical Image Segmentation with Limited Annotated Data

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher

  • Doctor Philosophy

    Advanced Strategies to Alleviate Challenges of Data Scarcity in Deep Learning for Medical Image Analysis

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher

  • Doctor Philosophy

    The role of duality in machine learning and computer vision.

    Principal Advisor

    Other advisors: Associate Professor Mahsa Baktashmotlagh

  • Doctor Philosophy

    Advanced Strategies to Alleviate Challenges of Data Scarcity in Deep Learning for Medical Image Analysis

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher

  • Doctor Philosophy

    Enhancing Building Fire Safety by Utilising Machine Learning Techniques

    Associate Advisor

    Other advisors: Dr Xin Yu

  • Doctor Philosophy

    Modelling cloud movement to generate short term solar irradiance predictions and subsequent expected PV power production

    Associate Advisor

    Other advisors: Professor Eve McDonald-Madden, Dr Hui Ma

  • Doctor Philosophy

    Modelling cloud movement to generate short term solar irradiance predictions and subsequent expected PV power production

    Associate Advisor

    Other advisors: Professor Eve McDonald-Madden, Dr Hui Ma

  • Doctor Philosophy

    Generating data-driven continuous optimization problems for benchmarking

    Associate Advisor

    Other advisors: Associate Professor Marcus Gallagher

  • Doctor Philosophy

    Generating data-driven continuous optimization problems for benchmarking

    Associate Advisor

    Other advisors: Associate Professor Marcus Gallagher

Completed supervision

Enquiries

Contact Professor Brian Lovell directly for media enquiries about:

  • Artificial Intelligence
  • Biometrics
  • Border control
  • Computer modelling
  • Computer vision
  • Deep Learning
  • Face Recognition
  • Face-recognition technology
  • Identification technology
  • Image processing
  • Information technology
  • National security surveillance
  • Networks - neural
  • Neural networks - artificial
  • Pattern Recognition
  • Pattern recognition - digital imaging
  • Signal Processing
  • Wearable Technologies

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