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Unmasking the imposters: task-specific feature learning for face presentation attack detection.

Abdullakutty, Faseela; Elyan, Eyad; Johnston, Pamela

Authors

Faseela Abdullakutty



Abstract

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 in intra-dataset evaluations using existing face presentation attack detection models. Nonetheless, these models did not achieve similar performance when evaluated across datasets due to limited generalisation. Consequently, this article presents task-specific feature learning using deep pre-trained models. Model performance was evaluated using three public datasets: the SiW dataset was used for intra-dataset evaluation, while CASIA and Replay Attack were used for cross-dataset evaluation. Custom task-specific feature learning, compared to deep and hybrid models, demonstrated improved cross-dataset performance and exhibited more generalisability. The results suggest future direction for further research toward improving the model's generalisation using custom task-specific feature learning.

Citation

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

Conference Name 2023 International joint conference on neural networks (IJCNN2023)
Conference Location Gold Coast, Australia
Start Date Jun 18, 2023
End Date Jun 23, 2023
Acceptance Date Mar 31, 2023
Online Publication Date Aug 2, 2023
Publication Date Dec 31, 2023
Deposit Date Sep 15, 2023
Publicly Available Date Sep 15, 2023
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series ISSN 2161-4407; 2161-4393
Book Title Proceedings of the 2023 International joint conference on neural networks (IJCNN2023)
DOI https://doi.org/10.1109/IJCNN54540.2023.10191953
Keywords Face presentation attack detection; Deep learning; Generalisation
Public URL https://rgu-repository.worktribe.com/output/2079462

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