Flexible modal face presentation attack detection can break through the limitations of traditional multi-modal methods on modal consistency in model training and deployment, and it can deploy the unified model flexibly to real scenarios of multiple modals on demand. However, there are still issues with improved model performance and high demand for computing resources. Therefore, this paper proposed a single stream flexible modal face presentation attack detection network based on vision Transformer. Furthermore, this paper proposed the atrous patch embedding module to address the operational redundancy problem and reduce the input vector dimension, designed the modal encoding token to distinguish different modal features, and adopted a non-padding strategy to solve the modal absence problem essentially. The experimental results on publicly available multi-modal datasets show that this method can obtain the best ACER averages of 2.69% and 33.81% in the intra-domain and cross-domain evaluations, respectively, and has excellent in-domain and out-of-domain generalization performance, and balances performance across different sub-protocols compared to the existing three methods. It significantly reduces the quantities of calculations and parameters compared with multi-stream methods, and is more suitable for flexible and efficient deployment in modal absence scenarios.

[2023-09-19] Accepted Paper /doc/2023.07.0319/v1

[2024-03-05] Printed Article /doc/2023.07.0319/v2