All Outputs (6)

Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection. (2023)
Thesis
ABDULLAKUTTY, F.C. 2023. Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2270640

Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, att... Read More about Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection..

Fusion methods for face presentation attack detection. (2022)
Journal Article
ABDULLAKUTTY, F., JOHNSTON, P. and ELYAN, E. 2022. Fusion methods for face presentation attack detection. Sensors [online], 22(14): soft sensors 2021-2022, article 5196. Available from: https://doi.org/10.3390/s22145196

Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literat... Read More about Fusion methods for face presentation attack detection..

Deep transfer learning on the aggregated dataset for face presentation attack detection. (2022)
Journal Article
ABDULLAKUTTY, F., ELYAN, E., JOHNSTON, P. and ALI-GOMBE, A. 2022. Deep transfer learning on the aggregated dataset for face presentation attack detection. Cognitive computation [online], 14(6), pages 2223-2233. Available from: https://doi.org/10.1007/s12559-022-10037-z

Presentation attacks are becoming a serious threat to one of the most common biometric applications, namely face recognition (FR). In recent years, numerous methods have been presented to detect and identify these attacks using publicly available dat... Read More about Deep transfer learning on the aggregated dataset for face presentation attack detection..

A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods. (2021)
Journal Article
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2021. A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods. Information fusion [online], 75, pages 55-69. Available from: https://doi.org/10.1016/j.inffus.2021.04.015

Face Recognition is considered one of the most common biometric solutions these days and is widely used across a range of devices for various security purposes. The performance of FR systems has improved by orders of magnitude over the past decade. T... Read More about A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods..

Unmasking the imposters: task-specific feature learning for face presentation attack detection.
Presentation / Conference Contribution
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2023. Unmasking the imposters: task-specific feature learning for face presentation attack detection. In Proceedings of the 2023 International joint conference on neural networks (IJCNN2023), 18-23 June 2023, Gold Coast, Australia. Piscataway: IEEE [online], 10191953. Available from: https://doi.org/10.1109/IJCNN54540.2023.10191953

Presentation attacks pose a threat to the reliability of face recognition systems. A photograph, a video, or a mask representing an authorised user can be used to circumvent the face recognition system. Recent research has demonstrated high accuracy... Read More about Unmasking the imposters: task-specific feature learning for face presentation attack detection..

Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis.
Presentation / Conference Contribution
ABDULLAKUTTY, F. and NASEEM, U. 2024. Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis. In Chua, T.-S., Ngo, C.-W., Kumar, R., Lauw, H.W. and Lee, R.K.-W. (eds.). WWW'24 companion: companion proceedings of the ACM web conference 2024, 13-17 May 2024, Singapore. New York: ACM [online], pages 1681-1689. Available from: https://doi.org/10.1145/3589335.3652504

Memes are important because they serve as conduits for expressing emotions, opinions, and social commentary online, providing valuable insight into public sentiment, trends, and social interactions. By combining textual and visual elements, multi-mod... Read More about Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis..