Does lossy image compression affect racial bias within face recognition? (2022)
Conference Proceeding
Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022). Does lossy image compression affect racial bias within face recognition?.

This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject. We adopt a recently proposed racial phenotype-based bias analysis methodology to measur... Read More about Does lossy image compression affect racial bias within face recognition?.

Measuring Hidden Bias within Face Recognition via Racial Phenotypes (2022)
Conference Proceeding
Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. (2022). Measuring Hidden Bias within Face Recognition via Racial Phenotypes. . https://doi.org/10.1109/wacv51458.2022.00326

Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such... Read More about Measuring Hidden Bias within Face Recognition via Racial Phenotypes.

Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation (2020)
Conference Proceeding
Yucer, S., Akcay, S., Al Moubayed, N., & Breckon, T. (2020). Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Conference Proceeding
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. In 2019 International Conference on Robotics and Automation (ICRA) ; proceedings (4889-4895). https://doi.org/10.1109/icra.2019.8794060

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Conference Proceeding
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder (2016)
Conference Proceeding
Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II (423-430). https://doi.org/10.1007/978-3-319-44781-0_50

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topi... Read More about SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder.