255 research outputs found
Hyperbolic Face Anti-Spoofing
Learning generalized face anti-spoofing (FAS) models against presentation
attacks is essential for the security of face recognition systems. Previous FAS
methods usually encourage models to extract discriminative features, of which
the distances within the same class (bonafide or attack) are pushed close while
those between bonafide and attack are pulled away. However, these methods are
designed based on Euclidean distance, which lacks generalization ability for
unseen attack detection due to poor hierarchy embedding ability. According to
the evidence that different spoofing attacks are intrinsically hierarchical, we
propose to learn richer hierarchical and discriminative spoofing cues in
hyperbolic space. Specifically, for unimodal FAS learning, the feature
embeddings are projected into the Poincar\'e ball, and then the hyperbolic
binary logistic regression layer is cascaded for classification. To further
improve generalization, we conduct hyperbolic contrastive learning for the
bonafide only while relaxing the constraints on diverse spoofing attacks. To
alleviate the vanishing gradient problem in hyperbolic space, a new feature
clipping method is proposed to enhance the training stability of hyperbolic
models. Besides, we further design a multimodal FAS framework with Euclidean
multimodal feature decomposition and hyperbolic multimodal feature fusion &
classification. Extensive experiments on three benchmark datasets (i.e., WMCA,
PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed
method can bring significant improvement compared to the Euclidean baselines on
unseen attack detection. In addition, the proposed framework is also
generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP
REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types
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
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
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
Recent Advancement in 3D Biometrics using Monocular Camera
Recent literature has witnessed significant interest towards 3D biometrics
employing monocular vision for robust authentication methods. Motivated by
this, in this work we seek to provide insight on recent development in the area
of 3D biometrics employing monocular vision. We present the similarity and
dissimilarity of 3D monocular biometrics and classical biometrics, listing the
strengths and challenges. Further, we provide an overview of recent techniques
in 3D biometrics with monocular vision, as well as application systems adopted
by the industry. Finally, we discuss open research problems in this area of
researchComment: Accepted and presented in IJCB 202
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