Top of page
Global Site Navigation

School of Science

Local Section Navigation
You are here: Main Content

Dr Syed Afaq Ali Shah

Senior Lecturer

Staff Member Details
Telephone: +61 8 6304 2379
Email: afaq.shah@ecu.edu.au
Campus: Joondalup  
Room: JO18.418C  
ORCID iD: https://orcid.org/0000-0003-2181-8445

Dr. Syed Afaq Shah is a Senior Lecturer (Computer Science) in the School of Science and core member of Centre for AI and Machine Learning, ECU.

Current Teaching

  • CSI6208 - Programming Principles (Postgraduate)
  • CSP2151 - Programming Fundamentals

Background

Dr. Afaq Shah is a Senior Lecturer at Edith Cowan University (ECU) and Adjunct Senior Lecturer (Department of Computer Science and Software Engineering), the University of Western Australia (UWA), Perth. He leads the Robotics and Artificial Intelligence Research (RAIR) group. Prior to joining ECU, he worked as a Lecturer at Murdoch University (2018 to 2021) and Lecturer ICT at Central Queensland University (2018). He received his PhD degree from UWA and later worked as a Research Fellow for 2.5 years at UWA. Prior to his enrolment at the University of Western Australia, he worked for seven years as Project Manager and later Deputy Chief Engineer (Information Technology) in the aviation industry.

Afaq’s main field of research and interest is ‘Artificial Intelligence’. He develops deep learning techniques for image/video/data analysis, scene understanding, health (e.g., prediction of cardiovascular and Alzheimer disease), agricultural monitoring, medical/bio-medical applications, remote sensing, security, surveillance and monitoring. He has significantly contributed to machine learning, 3D feature descriptors, 3D object recognition and reconstruction, image segmentation, biometrics, 2D-3D scene understanding, and classification, human computer interaction, 2D-3D action and gesture recognition, image captioning, and health analytics. He has published over 50 research papers in high impact factor journals including IJCV, IEEE TNNLS, Pattern Recognition and reputable conferences including NeurIPS and ECCV. He has also co-authored a book, “A Guide to Convolutional Neural Networks for Computer Vision”. He has been awarded over $350,000 in different competitive research funding schemes. He is Australian Computer Society Certified Professional and Fellow Higher Education Academy (FHEA) UK.

Professional Memberships

  • 2019-Present – Australian Computer Society Member
  • 2020-Present – Fellow Higher Education Academy (FHEA), UK
  • Life Member of Pakistan Engineering Council (Member of Washington Accord)

Awards and Recognition

National and International Awards

  • 2020 – DST Best Contribution to Science Award, Digital Image Computing: Techniques and Applications (DICTA) 2020
  • 2020 - ACU Early Career Conference Award
  • 2019 - Australian Computer Society Certified Professional
  • 2018 - NeurIPS Conference Travel Award
  • 2017 - UWA Research Collaboration Award
  • 2016 - UWA Start Something Award for Research Impact through Enterprise

University and National Teaching Awards

  • 2020 - Fellow Higher Education Academy (FHEA), UK
  • 2019 – Associate Fellow, Higher Education Academy (FHEA), UK
  • 2016 - Nominated for Teaching and Learning Excellence Award (UWA)

National and International Research Positions

  • 2021-Present – Associate Editor, Network: Computation in Neural Systems
  • 2021 – Workshop and Special Session Chair, DICTA2021
  • 2020 – Guest Editor, Remote Sensing Journal
  • 2020-Present – Lead Reviewer Serbian Research Fund and reviewer, Mitacs Accelerate grant (Canada).
  • 2017-2018 – Program committee member Advanced Concepts for Intelligent Vision Systems (ACIVS).

Research Areas and Interests

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Pattern recognition
  • Computer Vision
  • Health Analytics
  • Facial Analysis
  • Robotics and Autonomous Systems
  • Object Detection, Recognition and Segmentation
  • Image/video Processing
  • Digital AgriTech

Qualifications

  • Doctor of Philosophy, The University of Western Australia, 2016.

Research Outputs

Journal Articles

Journal Articles

  • Ye, G., Song, J., Feng, M., Zhu, G., Shen, P., Zhang, L., Shah, A., Bennamoun, M. (2023). Position and structure-aware graph learning. Neurocomputing, 556(November), Article number 126581. https://doi.org/https://doi.org/10.1016/j.neucom.2023.126581.
  • Li, J., Zhu, G., Hua, C., Feng, M., Bennamoun, B., Li, P., Lu, X., Song, J., Shen, P., Xu, X., Mei, L., Zhang, L., Shah, A., Bennamoun, M. (2023). A Systematic Collection of Medical Image Datasets for Deep Learning. ACM Computing Surveys, 56(5), Article number 116. https://doi.org/https://doi.org/10.1145/3615862.

Conference Publications

  • Gu, J., Jiang, M., Li, H., Lu, X., Zhu, G., Shah, A., Zhang, L., Bennamoun, M. (2023). UE4-NeRF: Neural Radiance Field for Real-Time Rendering of Large-Scale Scene. The Thirty-seventh Annual Conference on Neural Information Processing Systems (13 pages). NeurIPS.
  • Mahmood, H., Iqbal, A., Islam, S., Shah, A. (2023). 3D brain registration with intensity shift robustness. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (2805-2809). IEEE. https://doi.org/10.1109/ICIP49359.2023.10222341.
  • Mirnateghi, N., Shah, A., Bennamoun, M. (2023). Deep Bayesian Image Set Classification Approach for Defence against Adversarial Attacks. The International Conference on Digital Image Computing: Techniques and Applications (9). IEEE.
  • Shah, A., Khalifa, Z. (2023). Hierarchical Transformer for Visual Affordance Understanding using a Large Scale Dataset. IEEE/RSJ International Conference on Intelligent Robots and Systems (11371-11376). IEEE. https://doi.org/10.1109/IROS55552.2023.10341976.
  • Khalifa, Z., Shah, A. (2023). A Large Scale Multi-view RGBD Visual Affordance Learning Dataset. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (5). IEEE. https://doi.org/10.48550/arXiv.2203.14092.
  • Islam, S., Shah, A., Nguyen, CD. (2023). Deep Learning Approach for Automatic Segmentation of Dirt on Cattle Skin using Image Data. International Conference Image and Vision Computing New Zealand (6 pages). IEEE Computer Society. https://doi.org/10.1109/IVCNZ61134.2023.10344224.

Journal Articles

  • Zeng, Z., Wang, T., Ma, F., Zhang, L., Shen, P., Shah, A., Bennamoun, M. (2022). Probability-based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation. IEEE Transactions on Multimedia, 24(2022), 740-754. https://doi.org/10.1109/TMM.2021.3058770.
  • Imtiaz, M., Shah, A., Ur Rehman, Z. (2022). A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges. Neuroscience Informatics, 2(4), Article number 100079. https://doi.org/10.1016/j.neuri.2022.100079.
  • Zhang, L., Li, J., Lu, G., Shen, P., Bennamoun, M., Shah, A., Miao, Q., Zhu, G., Li, P., Lu, X. (2022). Analysis and Variants of Broad Learning System. IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(1), 334 - 344. https://doi.org/10.1109/TSMC.2020.2995205.
  • Ayris, D., Imtiaz, M., Horbury, K., Williams, B., Blackney, M., See, C., Shah, A. (2022). Novel Deep Learning Approach to Model and Predict the spread of COVID-19. Intelligent Systems with Applications, 14(May 2022), article number 200068. https://doi.org/10.1016/j.iswa.2022.200068.
  • Shah, A., Deng, W., Cheema, M., Bais, A. (2022). CommuNety: deep learning-based face recognition system for the prediction of cohesive communities. Multimedia Tools and Applications, 2022(Article in Press), 1-19. https://doi.org/10.1007/s11042-022-13741-y.
  • Shah, A., Pickupana, P., Luo, H., Ekeze, A., Sohel, F., Laga, H., Li, C., Paynter, B., Wang, P. (2022). Automatic and Fast Classification of Barley Grains from Images: A Deep Learning Approach. Smart Agricultural Technology, 2(December 2022), article number 100036. https://doi.org/10.1016/j.atech.2022.100036.

Journal Articles

  • Xue, Z., Li, P., Zhang, L., Lu, X., Zhu, G., Shen, P., Shah, A., Bennamoun, M. (2021). Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images. IEEE Transactions on Medical Imaging, 40(12), 3531-3542. https://doi.org/10.1109/TMI.2021.3089702.
  • Fan, Z., Li, J., Zhang, L., Zhu, G., Li, P., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T., Wei, W. (2021). U-net based analysis of MRI for Alzheimer’s disease diagnosis. Neural Computing and Applications, 33(20), 13587-13599. https://doi.org/10.1007/s00521-021-05983-y.
  • Li, P., Kong, X., Li, J., Zhu, G., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T. (2021). A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours. Academy of Nutrition and Dietetics, 2(February 2021), Article number 609349. https://doi.org/10.3389/fdgth.2020.609349.
  • Nadeem, U., Shah, A., Bennamoun, M., Togneri, R., Sohel, F. (2021). Real time surveillance for low resolution and limited data scenarios: An image set classification approach. Information Sciences, 580(November 2021), 578-597. https://doi.org/10.1016/j.ins.2021.08.093.
  • Lubna, ., Mufti, N., Shah, A. (2021). Automatic number plate recognition:A detailed survey of relevant algorithms. Sensors, 21(9), Article number 3028. https://doi.org/10.3390/s21093028.

Conference Publications

  • Sharif, N., Bennamoun, M., Liu, W., Shah, A. (2021). SubICap: Towards Subword-informed Image Captioning. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (Article number 20696987). IEEE. https://doi.org/10.1109/WACV48630.2021.00358.

Book Chapters

  • Sharif, N., Nadeem, U., Shah, A., Bennamoun, M., Liu, W. (2020). Vision to Language: Methods, Metrics and Datasets. Machine Learning Paradigms. Advances in Deep Learning-based Technological Applications (9-62). Springer. https://doi.org/10.1007/978-3-030-49724-8_2.

Journal Articles

  • Zhang, L., Zhang, J., Shen, P., Zhu, G., Li, P., Lu, X., Zhang, H., Shah, A., Bennamoun, M. (2020). Block Level Skip Connections across Cascaded V-Net for Multi-Organ Segmentation. IEEE Transactions on Medical Imaging, 39(9), 2782-2793. https://doi.org/10.1109/TMI.2020.2975347.
  • Zhu, G., Zhang, L., Yang, L., Mei, L., Shah, A., Bennamoun, M., Shen, P. (2020). Redundancy and Attention in Convolutional LSTM for Gesture Recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1323-1335. https://doi.org/10.1109/TNNLS.2019.2919764.
  • Chen, Y., Sohel, F., Shah, A., Ding, S. (2020). Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography. Optik, 219(Oct 2021), Article number 164828. https://doi.org/10.1016/j.ijleo.2020.164828.
  • Li, H., Zhang, L., Zhang, X., Zhang, M., Zhu, G., Shen, P., Li, P., Bennamoun, M., Shah, A. (2020). Color vision deficiency datasets & recoloring evaluation using GANs. Multimedia Tools and Applications, 79(37-38), 27583-27614. https://doi.org/10.1007/s11042-020-09299-2.
  • Zhu, G., Zhang, L., Li, H., Shen, P., Shah, A., Bennamoun, M. (2020). Topology-learnable graph convolution for skeleton-based action recognition. Pattern Recognition Letters, 135(Jul 2020), 286-292. https://doi.org/10.1016/j.patrec.2020.05.005.

Conference Publications

  • Shah, A. (2020). Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition. Image and Video Technology PSIVT 2019 International Workshops Sydney, NSW, Australia, November 18–22, 2019 Revised Selected Papers (183-193). Springer. https://doi.org/10.1007/978-3-030-39770-8_15.
  • Sharif, N., Jalwana, M., Bennamoun, M., Liu, W., Shah, A. (2020). Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning. Proceedings of 35th International Conference on Image and Vision Computing New Zealand (Article number 9290719). IEEE. https://doi.org/10.1109/IVCNZ51579.2020.9290719.
  • Zhang, L., Liu, Y., Xiao, H., Yang, L., Zhu, G., Shah, A., Bennamoun, M., Shen, P. (2020). Efficient Scene Text Detection with Textual Attention Tower. Proceedings of International Conference on Acoustics, Speech and Signal Processing (4272-4276). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054213.
  • Shah, A., Bougre, M., Akhtar, N., Bennamoun, M., Zhang, L. (2020). Efficient Detection of Pixel-Level Adversarial Attacks. Proceedings of International Conference on Image Processing, ICIP (718-722). IEEE Computer Society. https://doi.org/10.1109/ICIP40778.2020.9191084.
  • Zhang, L., Wang, X., Li, H., Zhu, G., Shen, P., Li, P., Lu, X., Shah, A., Bennamoun, M. (2020). Structure-Feature based Graph Self-adaptive Pooling. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (3098-3104). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380083.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2020). WEmbSim: A Simple yet Effective Metric for Image Captioning. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA) Conference (Article number 9363392). Institute of Electrical and Electronics Engineers Inc. (IEEE). https://doi.org/10.1109/DICTA51227.2020.9363392.

Book Chapters

Journal Articles

  • Zhu, G., Zhang, L., Shen, P., Song, J., Shah, A., Bennamoun, M. (2019). Continuous gesture segmentation and recognition using 3dcnn and convolutional lstm. IEEE Transactions on Multimedia, 21(4), 1011-1021. https://doi.org/10.1109/TMM.2018.2869278.
  • Shah, A., Bennamoun, M., Molton, M. (2019). Machine learning approaches for prediction of facial rejuvenation using real and synthetic data. IEEE Access, 7(2019), 23779-23787. https://doi.org/10.1109/ACCESS.2019.2899379.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2019). LCEval: Learned Composite Metric for Caption Evaluation. International Journal of Computer Vision, 127(10), 1586-1610. https://doi.org/10.1007/s11263-019-01206-z.

Conference Publications

  • Shah, A., Bennamoun, M., Molton, M. (2019). A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634657). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634657.
  • Zhang, L., Zhang, S., Shen, P., Zhu, G., Shah, A., Bennamoun, M. (2019). Relationship detection based on object semantic inference and attention mechanisms. ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval (68-72). Association for Computing Machinery, Inc. https://doi.org/10.1145/3323873.3325025.
  • Shah, A., Bennamoun, M., Molton, M. (2019). A Training-Free Mesh Upsampling and Morphing Technique for 3D Face Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634685). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634685.

Journal Articles

  • Zhang, L., Feng, Y., Shen, P., Zhu, G., Wei, W., Song, J., Shah, A., Bennamoun, M. (2018). Efficient finer-grained incremental processing with MapReduce for big data. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 80(March 2018), 102-111. https://doi.org/10.1016/j.future.2017.09.079.
  • Zhang, L., Wang, L., Zhang, X., Shen, P., Bennamoun, M., Zhu, G., Shah, A., Song, J. (2018). Semantic scene completion with dense CRF from a single depth image. Neurocomputing, 318(27-Nov-18), 182-195. https://doi.org/10.1016/j.neucom.2018.08.052.
  • Zhang, L., Li, H., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M., Zhang, L. (2018). Improving Semantic Image Segmentation with a Probabilistic Superpixel-Based Dense Conditional Random Field. IEEE Access, 6(2018), 15297-15310. https://doi.org/10.1109/ACCESS.2018.2814568.
  • Zhang, L., Xu, Q., Zhu, G., Song, J., Zhang, X., Shen, P., Wei, W., Shah, A., Bennamoun, M. (2018). Improved colour-to-grey method using image segmentation and colour difference model for colour vision deficiency. IET Image Processing, 12(3), 314-319. https://doi.org/10.1049/iet-ipr.2017.0482.
  • Shah, A., Bennamoun, M., Boussaid, F., While, L. (2018). Evolutionary Feature Learning for 3-D Object Recognition. IEEE Access, 6(2018), 2434-2444. https://doi.org/10.1109/ACCESS.2017.2783331.

Conference Publications

  • Zhang, L., Kong, X., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M. (2018). Reflective Field for Pixel-Level Tasks. Proceedings - International Conference on Pattern Recognition (529-534). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545817.
  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). NNEval: Neural network based evaluation metric for image captioning. Proceedings of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (39-55). Springer Verlag. https://doi.org/10.1007/978-3-030-01237-3_3.
  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). Learning-based composite metrics for improved caption evaluation. Proceedings of ACL 2018, Student Research Workshop (14-20). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-3003.
  • Zhang, L., Zhu, G., Mei, L., Shen, P., Shah, A., Bennamoun, M. (2018). Attention in convolutional LSTM for gesture recognition. Proceedings of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) (1953-1962). Neural information processing systems foundation.

Journal Articles

Conference Publications

  • Zhang, L., Zhu, G., Shen, P., Song, J., Shah, A., Bennamoun, M. (2017). Learning spatiotemporal features using 3DCNN and convolutional LSTM for gesture recognition. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (3120-3128). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2017.369.
  • Hu, H., Shah, A., Bennamoun, M., Molton, M. (2017). 2D and 3D face recognition using convolutional neural network. IEEE Region 10 Annual International Conference, Proceedings/TENCON (133-138). IEEE. https://doi.org/10.1109/TENCON.2017.8227850.
  • Shah, A., Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R. (2017). Efficient Image Set Classification Using Linear Regression Based Image Reconstruction. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (601-610). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2017.88.

Journal Articles

  • Shah, A., Bennamoun, M., Boussaid, F. (2016). A novel feature representation for automatic 3D object recognition in cluttered scenes. Neurocomputing, 205(12-Sep-16), 1-15. https://doi.org/10.1016/j.neucom.2015.11.019.
  • Molton, M., Shah, A., Bennamoun, M. (2016). Improving the Face of Cosmetic Medicine: An Automatic Three-dimensional Analysis System for Facial Rejuvenation. Journal of Aesthetic & Reconstructive Surgery, 2(2), 6p.. https://doi.org/10.4172/2472-1905.100021.
  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Iterative deep learning for image set based face and object recognition. Neurocomputing, 174(22-Jan-16), 866-874. https://doi.org/10.1016/j.neucom.2015.10.004.

Conference Publications

  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Automatic 3D face landmark localization based on 3D vector field analysis. Proceedings of International Conference Image and Vision Computing New Zealand (6p.). IEEE Computer Society. https://doi.org/10.1109/IVCNZ.2015.7761526.

Research Student Supervision

Principal Supervisor

  • Doctor of Philosophy, Towards AI Explainability: Unraveling Black-Box Models
  • Doctor of Philosophy, Deep learning techniques for classification and evaluaton of artificial empathy

Associate Supervisor

  • Doctor of Philosophy, The Development of an Artificial Intelligence Model to Automate the Detection of Punches in Boxing
  • Doctor of Philosophy, Cyberattack knowledge graph-based modelling of IoT networks used in remote patient monitoring

Principal Supervisor

  • Masters by Research, EMOTENET: Deep Neural Network for Facial Emotion Recognition Using Image Set Classification
  • Masters by Research, Deep Bayesian Image Set Classification: A Novel Approach for Defence Against Adversarial Attacks on Deep Learning Systems

Associate Supervisor

  • Doctor of Philosophy, Natural Language Description of Images
  • Doctor of Philosophy, Domain shift robustness in deep learnng models
  • Masters by Research, 2D and 3D Face Recognition Using Convolutional Neural Network
Skip to top of page