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Outputs (11)

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics (2024)
Journal Article
Yucer, S., Atapour-Abarghouei, A., Al Moubayed, N., & Breckon, T. P. (2024). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. arXiv,

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges... Read More about Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics.

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?.

In-Materio Extreme Learning Machines (2022)
Book Chapter
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

Towards Intelligently Designed Evolvable Processors (2022)
Journal Article
Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., …Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material’s properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio pro... Read More about Towards Intelligently Designed Evolvable Processors.

Enhanced Methods for Evolution in-Materio Processors (2022)
Conference Proceeding
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Enhanced Methods for Evolution in-Materio Processors. . https://doi.org/10.1109/icrc53822.2021.00026

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and... Read More about Enhanced Methods for Evolution in-Materio Processors.

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.

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.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.