79 research outputs found
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
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mainly focus on the handcrafted features, which are outdated and uninspiring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
Self-Domain Adaptation for Face Anti-Spoofing
Although current face anti-spoofing methods achieve promising results under
intra-dataset testing, they suffer from poor generalization to unseen attacks.
Most existing works adopt domain adaptation (DA) or domain generalization (DG)
techniques to address this problem. However, the target domain is often unknown
during training which limits the utilization of DA methods. DG methods can
conquer this by learning domain invariant features without seeing any target
data. However, they fail in utilizing the information of target data. In this
paper, we propose a self-domain adaptation framework to leverage the unlabeled
test domain data at inference. Specifically, a domain adaptor is designed to
adapt the model for test domain. In order to learn a better adaptor, a
meta-learning based adaptor learning algorithm is proposed using the data of
multiple source domains at the training step. At test time, the adaptor is
updated using only the test domain data according to the proposed unsupervised
adaptor loss to further improve the performance. Extensive experiments on four
public datasets validate the effectiveness of the proposed method.Comment: Camera Ready, AAAI 202
Restrictive Voting Technique for Faces Spoofing Attack
Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications
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