146 research outputs found
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Face anti-spoofing is crucial to the security of face recognition systems.
Most previous methods formulate face anti-spoofing as a supervised learning
problem to detect various predefined presentation attacks, which need large
scale training data to cover as many attacks as possible. However, the trained
model is easy to overfit several common attacks and is still vulnerable to
unseen attacks. To overcome this challenge, the detector should: 1) learn
discriminative features that can generalize to unseen spoofing types from
predefined presentation attacks; 2) quickly adapt to new spoofing types by
learning from both the predefined attacks and a few examples of the new
spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot
learning problem. In this paper, we propose a novel Adaptive Inner-update Meta
Face Anti-Spoofing (AIM-FAS) method to tackle this problem through
meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task
of detecting unseen spoofing types by learning from predefined living and
spoofing faces and a few examples of new attacks. To assess the proposed
approach, we propose several benchmarks for zero- and few-shot FAS. Experiments
show its superior performances on the presented benchmarks to existing methods
in existing zero-shot FAS protocols.Comment: Accepted by AAAI202
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