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.
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