Face Presentation Attack Detection using Taskonomy Feature
- URL: http://arxiv.org/abs/2111.11046v1
- Date: Mon, 22 Nov 2021 08:35:26 GMT
- Title: Face Presentation Attack Detection using Taskonomy Feature
- Authors: Wentian Zhang, Haozhe Liu, Raghavendra Ramachandra, Feng Liu, Linlin
Shen, Christoph Busch
- Abstract summary: Presentation Attack Detection (PAD) methods are critical to ensure the security of Face Recognition Systems (FRSs)
Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAs.
We propose to apply taskonomy (task taxonomy) from other face-related tasks to solve face PAD.
- Score: 26.343512092423985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The robustness and generalization ability of Presentation Attack Detection
(PAD) methods is critical to ensure the security of Face Recognition Systems
(FRSs). However, in the real scenario, Presentation Attacks (PAs) are various
and hard to be collected. Existing PAD methods are highly dependent on the
limited training set and cannot generalize well to unknown PAs. Unlike PAD
task, other face-related tasks trained by huge amount of real faces (e.g. face
recognition and attribute editing) can be effectively adopted into different
application scenarios. Inspired by this, we propose to apply taskonomy (task
taxonomy) from other face-related tasks to solve face PAD, so as to improve the
generalization ability in detecting PAs. The proposed method, first introduces
task specific features from other face-related tasks, then, we design a
Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such
features to adapt to PAD task. Finally, face PAD is achieved by using the
hierarchical features from a CNN-based PA detector and the re-mapped features.
The experimental results show that the proposed method can achieve significant
improvements in the complicated and hybrid datasets, when compared with the
state-of-the-art methods. In particular, when trained using OULU-NPU,
CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error Rate) of
5.48% in MSU-MFSD, outperforming the baseline by 7.39%. Code will be made
publicly available.
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