1,945 research outputs found
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
FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning
In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features
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
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