Outputs (2)

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..

Unmasking the imposters: task-specific feature learning for face presentation attack detection. (2023)
Conference Proceeding
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..