453 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mainly focus on the handcrafted features, which are outdated and uninspiring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
Regularized Fine-grained Meta Face Anti-spoofing
Face presentation attacks have become an increasingly critical concern when
face recognition is widely applied. Many face anti-spoofing methods have been
proposed, but most of them ignore the generalization ability to unseen attacks.
To overcome the limitation, this work casts face anti-spoofing as a domain
generalization (DG) problem, and attempts to address this problem by developing
a new meta-learning framework called Regularized Fine-grained Meta-learning. To
let our face anti-spoofing model generalize well to unseen attacks, the
proposed framework trains our model to perform well in the simulated domain
shift scenarios, which is achieved by finding generalized learning directions
in the meta-learning process. Specifically, the proposed framework incorporates
the domain knowledge of face anti-spoofing as the regularization so that
meta-learning is conducted in the feature space regularized by the supervision
of domain knowledge. This enables our model more likely to find generalized
learning directions with the regularized meta-learning for face anti-spoofing
task. Besides, to further enhance the generalization ability of our model, the
proposed framework adopts a fine-grained learning strategy that simultaneously
conducts meta-learning in a variety of domain shift scenarios in each
iteration. Extensive experiments on four public datasets validate the
effectiveness of the proposed method.Comment: Accepted by AAAI 2020. Codes are available at
https://github.com/rshaojimmy/AAAI2020-RFMetaFA
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing
Biometric systems are vulnerable to Presentation Attacks (PA) performed using
various Presentation Attack Instruments (PAIs). Even though there are numerous
Presentation Attack Detection (PAD) techniques based on both deep learning and
hand-crafted features, the generalization of PAD for unknown PAI is still a
challenging problem. In this work, we empirically prove that the initialization
of the PAD model is a crucial factor for the generalization, which is rarely
discussed in the community. Based on such observation, we proposed a
self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is
based on a global-local view coupled with De-Folding and De-Mixing to derive
the task-specific representation for PAD. During De-Folding, the proposed
technique will learn region-specific features to represent samples in a local
pattern by explicitly minimizing generative loss. While De-Mixing drives
detectors to obtain the instance-specific features with global information for
more comprehensive representation by minimizing interpolation-based
consistency. Extensive experimental results show that the proposed method can
achieve significant improvements in terms of both face and fingerprint PAD in
more complicated and hybrid datasets when compared with state-of-the-art
methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed
method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD,
exceeding baseline performance by 9.54%. The source code of the proposed
technique is available at https://github.com/kongzhecn/dfdm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems
(TNNLS
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