70 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
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face
recognition system by presenting spoofed faces. State-of-the-art FAS techniques
predominantly rely on deep learning models but their cross-domain
generalization capabilities are often hindered by the domain shift problem,
which arises due to different distributions between training and testing data.
In this study, we develop a generalized FAS method under the Efficient
Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained
Vision Transformer models for the FAS task. During training, the adapter
modules are inserted into the pre-trained ViT model, and the adapters are
updated while other pre-trained parameters remain fixed. We find the
limitations of previous vanilla adapters in that they are based on linear
layers, which lack a spoofing-aware inductive bias and thus restrict the
cross-domain generalization. To address this limitation and achieve
cross-domain generalized FAS, we propose a novel Statistical Adapter
(S-Adapter) that gathers local discriminative and statistical information from
localized token histograms. To further improve the generalization of the
statistical tokens, we propose a novel Token Style Regularization (TSR), which
aims to reduce domain style variance by regularizing Gram matrices extracted
from tokens across different domains. Our experimental results demonstrate that
our proposed S-Adapter and TSR provide significant benefits in both zero-shot
and few-shot cross-domain testing, outperforming state-of-the-art methods on
several benchmark tests. We will release the source code upon acceptance
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
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks, also known as spoofing attacks, pose a significant
threat to biometric systems that rely on facial recognition systems, such as
access control systems, mobile payments, and identity verification systems. To
prevent spoofing, several video-based methods have been presented in the
literature that analyze facial motion in successive video frames. However,
estimating the motion between adjacent frames is a challenging task and
requires high computational cost. In this paper, we reformulate the face
anti-spoofing task as a motion prediction problem and introduce a deep ensemble
learning model with a frame skipping mechanism. The proposed frame skipping is
based on a uniform sampling approach where the original video is divided into
fixed size video clips. In this way, every nth frame of the clip is selected to
ensure that the temporal patterns can easily be perceived during the training
of three different recurrent neural networks (RNNs). Motivated by the
performance of each RNNs, a meta-model is developed to improve the overall
recognition performance by combining the predictions of the individual RNNs.
Extensive experiments were conducted on four datasets, and state-of-the-art
performance is reported for MSU-MFSD (3.12\%), Replay-Attack (11.19\%), and
OULU-NPU (12.23\%) using half total error rate (HTER) in the most challenging
cross-dataset test scenario
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