̽»¨ÏµÁÐ

Dr. Liu Bangli earned her Ph.D. in Computer Science from the University of Portsmouth, UK, in 2018. She is currently a lecturer at ̽»¨ÏµÁÐ in Leicester, UK, after previously holding a lecturing position at the University of Portsmouth. Dr. Liu's academic experience includes a research visit to Tokyo Metropolitan University in Japan in 2018 and postdoctoral positions at both the University of Portsmouth and Loughborough University.

Dr. Liu's research primarily focuses on artificial intelligence, human-robot interaction, human behavior analysis and applications, computer vision, and related fields. She is currently leading several research projects funded by the Royal Society, ̽»¨ÏµÁÐ, and other sources. Her work has been published in top-tier international academic journals, including Pattern Recognition, Neurocomputing, and IEEE Transactions on Industrial Informatics, among others. She was also invited to contribute a review chapter on computer vision-based human behavior analysis to the IEEE Press Handbook Human Machine System and has organized and chaired computer vision workshops at various international conferences, including IEEE SMC.

Institute of Artificial Intelligence (IAI)

Book Chapter

Liu, B., and Liu, H. (2022). Chapter 26: RGB-D based Human Action Recognition: from Handcrafted to Deep Learning, IEEE Press Handbook Human Machine System. (Accepted for publish).

Refereed Journals

Guo, Y., Cai, H., Li, B., LC Bramley, G., J Atkins, H., & Liu, B. (2026). Vision-language model-enhanced knowledge distillation for herbarium image classification.

Pasbanigoloojeh, R., Liu, B.*, & Yang, Y. (2026). Understanding and overcoming the breakdown of direct video embedding through pretrained representations. Journal of Visual Communication and Image Representation, 105002.

Zhou T, Li M, Ruan S, Duan J, Meng Y, Ji Z, Liu B, Balluet M, Lei B. FRMF-Net: Feature rectification and adaptive modality fusion guided multi-modal brain tumor segmentation network. Expert Systems with Applications. 2026 May 15:132884.

Zhou T, Ruan S, Meng Y, Duan J, Hu H, Jiang B, Ji Z, Liu B, Luo T, Lei B. UTriGate-Net: Uncertainty-aware brain tumor segmentation via triaxial context encoding and gated modality fusion. Expert Systems With Applications. 2026 Mar 28:132258.

Wang, J., Li, Z., Liu, B., Cai, H., Saada, M., & Meng, Q. (2025). High-performance inference graph convolutional networks for skeleton-based action recognition. Neurocomputing, 131078.

Liu, B., Cai, H., Ju, Z., & Liu, H. (2020). Multi-stage adaptive regression for online activity recognition. Pattern Recognition (Impact factor: 8.518), 98, 107053.

Liu, B., & Ait-Boudaoud, D. (2020). Effective image super resolution via hierarchical convolutional neural network. Neurocomputing (Impact factor: 5.779), 374, 109-116.

Liu, B., Cai, H., Ju, Z., and Liu, H. (2019). RGB-D Sensing based Human Action and Interaction Analysis: A Survey, Pattern Recognition (Impact factor: 8.518), 94, 1-12.

Liu, B., Ju, Z., and Liu, H. (2018). A structured multi-feature representation for recognizing human action and interaction. Neurocomputing (Impact factor: 5.779), 318, 287-296.

Cai, H., Jiang, L., Liu, B., Deng, Y., and Meng, Q. (2019). Assembling convolution neural networks for automatic viewing transformation, IEEE Transactions on Industrial Informatics (Impact factor: 11.648), 16.1 (2019): 587-594.

Cai, H., Liu, B., Zhang, J., Chen, S., and Liu, H. (2017). Visual focus of attention estimation using eye center localization. IEEE Systems Journal (Impact factor: 3.391), 11(3), 1320- 1325.

Yang, L., Wang, Z., Gao, S., Shi, M., & Liu, B. (2019). Magnetic flux leakage image classification method for pipeline weld based on optimized convolution kernel. Neurocomputing (Impact factor: 5.719), 365, 229-238.

Refereed Conference Papers

Wang, Z., Liu, B., Balluet, M., & Zhou, T. (2026, May). FL-AMNet: Federated Learning-based Brain Tumor Segmentation with Anatomical Symmetry-guided Multi-modal Fusion. In 2026 IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI) (pp. 1-6). IEEE.

Liu, B., Ju, Z., Kubota, N., and Liu, H. (2018). Online Action Recognition based on Skeleton Motion Distribution. British Machine Vision Conference Workshop (BMVCW).

Liu, B., Cai, H., Ji, X., Liu, H. (2017). Human-human interaction recognition based on spatial and motion trend feature. Int. Conf. Image Processing (ICIP), pp. 4547-4551.

Liu, B., Yu, H., Zhou, X., Liu, H. (2016). Combining 3D joints Moving Trend and Geometry property for human action recognition. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC). pp. 000332-000337.

Cai, H., Liu, B., Ju, Z., Thill, S., Belpaeme, T., Vanderborght, B., Liu, H. (2018) Accurate Eye Center Localization via Hierarchical Adaptive Convolution. British Machine Vision Conference (BMVC), 2018, 284.

• Artificial Intelligence
• Human Behavior Analysis and applications
• Human-robot Interaction
• Computer Vision
• Deep Learning

• Big Data
• Machine Learning
• Data Science

Fellowship of the Higher Education Academy (FHEA)

CSIP5101 Business analysis and Modelling (Msc, FHEQ level 7)

CSIP5203 Big Data Applications (Msc, FHEQ level 7, Module Leader)

CTEC2921 Big Data and Machine Learning (Bsc, FHEQ level 5, Module Leader)

IMAT5322 Big Data Analytics (Msc, FHEQ level 7, Module Leader)

IMAT5167 Data Warehouse Design and OLAP (Msc, FHEQ level 7)

IMAT5103 Database System and Design (Msc, FHEQ level 7)

CTEC3303 Systems Building: Methods (BSc, FHEQ level 6). 

• IEEE Student Travel Grant Award in IAPR in HongKong, China, 2017
• IEEE SMC Student Travel Grant in Budapest, Hungary, 2016

IEEE Member

[Principle Investigator]

‘Deep learning based human intention recognition in Human-Robot interaction’, (PI, first supervisor), ̽»¨ÏµÁÐ fully-funded 3-year PhD scholarship, Awarded, March 2023.

‘Towards Personalized Human Activity Recognition for Stroke Rehabilitation: A Proof of Concept Study’, (PI), ̽»¨ÏµÁÐ Research and Innovation fund (£2,500), Awarded, 2026

“Development Of Advanced Elderly Care Robot In Home Environments”, (PI), Research Grant, £19,040, Funded by The Royal Society, 12/2022-11/2023.

“Development of Intelligent Robotic intervention platform for Children with Autism”, (PI), £7,000, funded by ̽»¨ÏµÁÐ HIEF funding, 12/2022-07/2023.

Rahim Pasbanigoloojeh, Full time, 1st supervisor, 2023-present

Elijah Chukwuemeka Ununu, Full time, 1st supervisor, 2026-present

Kelvin Edeh, Full time, 2nd supervisor, 2026-present

CHINEDU OKENYI, Full time, 3rd supervisor, 2026-present

“Development Of Advanced Elderly Care Robot In Home Environments”, (Principle Investigator), Research Grant, £19,040, Funded by The Royal Society, 12/2022-11/2023.

• ‘Towards Personalized Human Activity Recognition for Stroke Rehabilitation: A Proof of Concept Study’, (PI), ̽»¨ÏµÁÐ Research and Innovation fund (£2,500), Awarded, 2026.

• ‘Development of Intelligent Robotic intervention platform for Children with Autism’, (PI) HIEF funding in ̽»¨ÏµÁÐ CEM Faculty, £7,000, Awarded, December 2022.
• ‘Deep learning based human intention recognition in Human-Robot interaction’, (PI, first supervisor), ̽»¨ÏµÁÐ fully-funded 3-year PhD scholarship, Awarded, March 2023.

0000-0002-2543-8987