Shuffled Patch-Wise Supervision for Presentation Attack Detection


Kantarcı A., Dertli H., Ekenel H. K.

20th Annual International Conference of the Biometrics-Special-Interest-Group (BIOSIG ), ELECTR NETWORK, 15 - 17 September 2021, vol.315 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 315
  • Doi Number: 10.1109/biosig52210.2021.9548317
  • Country: ELECTR NETWORK
  • Keywords: Face antispoofing, presentation attack detection, convolutional neural networks, real-world dataset
  • Istanbul Technical University Affiliated: Yes

Abstract

Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets -Replay-Mobile, OULU-NPU- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments