Few-Shot Domain Expansion for Face Anti-Spoofing
- URL: http://arxiv.org/abs/2106.14162v1
- Date: Sun, 27 Jun 2021 07:38:50 GMT
- Title: Few-Shot Domain Expansion for Face Anti-Spoofing
- Authors: Bowen Yang, Jing Zhang, Zhenfei Yin, Jing Shao
- Abstract summary: Face anti-spoofing (FAS) is an indispensable and widely used module in face recognition systems.
We identify and address a more practical problem: Few-Shot Domain Expansion for Face Anti-Spoofing (FSDE-FAS)
- Score: 28.622220790439055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Face anti-spoofing (FAS) is an indispensable and widely used module in face
recognition systems. Although high accuracy has been achieved, a FAS system
will never be perfect due to the non-stationary applied environments and the
potential emergence of new types of presentation attacks in real-world
applications. In practice, given a handful of labeled samples from a new
deployment scenario (target domain) and abundant labeled face images in the
existing source domain, the FAS system is expected to perform well in the new
scenario without sacrificing the performance on the original domain. To this
end, we identify and address a more practical problem: Few-Shot Domain
Expansion for Face Anti-Spoofing (FSDE-FAS). This problem is challenging since
with insufficient target domain training samples, the model may suffer from
both overfitting to the target domain and catastrophic forgetting of the source
domain. To address the problem, this paper proposes a Style transfer-based
Augmentation for Semantic Alignment (SASA) framework. We propose to augment the
target data by generating auxiliary samples based on photorealistic style
transfer. With the assistant of the augmented data, we further propose a
carefully designed mechanism to align different domains from both
instance-level and distribution-level, and then stabilize the performance on
the source domain with a less-forgetting constraint. Two benchmarks are
proposed to simulate the FSDE-FAS scenarios, and the experimental results show
that the proposed SASA method outperforms state-of-the-art methods.
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