1,138 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mainly focus on the handcrafted features, which are outdated and uninspiring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
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
This paper addresses the problem of face presentation attack detection using
different image modalities. In particular, the usage of short wave infrared
(SWIR) imaging is considered. Face presentation attack detection is performed
using recent models based on Convolutional Neural Networks using only carefully
selected SWIR image differences as input. Conducted experiments show superior
performance over similar models acting on either color images or on a
combination of different modalities (visible, NIR, thermal and depth), as well
as on a SVM-based classifier acting on SWIR image differences. Experiments have
been carried on a new public and freely available database, containing a wide
variety of attacks. Video sequences have been recorded thanks to several
sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal
spectra, as well as depth data. The best proposed approach is able to almost
perfectly detect all impersonation attacks while ensuring low bonafide
classification errors. On the other hand, obtained results show that
obfuscation attacks are more difficult to detect. We hope that the proposed
database will foster research on this challenging problem. Finally, all the
code and instructions to reproduce presented experiments is made available to
the research community
Fairness in Face Presentation Attack Detection
Face presentation attack detection (PAD) is critical to secure face
recognition (FR) applications from presentation attacks. FR performance has
been shown to be unfair to certain demographic and non-demographic groups.
However, the fairness of face PAD is an understudied issue, mainly due to the
lack of appropriately annotated data. To address this issue, this work first
presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining
several well-known PAD datasets where we provide seven human-annotated
attribute labels. This work then comprehensively analyses the fairness of a set
of face PADs and its relation to the nature of training data and the
Operational Decision Threshold Assignment (ODTA) on different data groups by
studying four face PAD approaches on our CAAD-PAD. To simultaneously represent
both the PAD fairness and the absolute PAD performance, we introduce a novel
metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on
CAAD-PAD show that the training data and ODTA induce unfairness on gender,
occlusion, and other attribute groups. Based on these analyses, we propose a
data augmentation method, FairSWAP, which aims to disrupt the identity/semantic
information and guide models to mine attack cues rather than attribute-related
information. Detailed experimental results demonstrate that FairSWAP generally
enhances both the PAD performance and the fairness of face PAD
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
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
The vulnerability of face recognition systems to presentation attacks has
limited their application in security-critical scenarios. Automatic methods of
detecting such malicious attempts are essential for the safe use of facial
recognition technology. Although various methods have been suggested for
detecting such attacks, most of them over-fit the training set and fail in
generalizing to unseen attacks and environments. In this work, we use transfer
learning from the vision transformer model for the zero-shot anti-spoofing
task. The effectiveness of the proposed approach is demonstrated through
experiments in publicly available datasets. The proposed approach outperforms
the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and
SiW-M datasets by a large margin. Besides, the model achieves a significant
boost in cross-database performance as well.Comment: 8 pages, 3 figures, Accepted for Publication in IJCB202
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