570 research outputs found
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
A Review on Face Anti-Spoofing
The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Face recognition has evolved as a widely used biometric modality. However,
its vulnerability against presentation attacks poses a significant security
threat. Though presentation attack detection (PAD) methods try to address this
issue, they often fail in generalizing to unseen attacks. In this work, we
propose a new framework for PAD using a one-class classifier, where the
representation used is learned with a Multi-Channel Convolutional Neural
Network (MCCNN). A novel loss function is introduced, which forces the network
to learn a compact embedding for bonafide class while being far from the
representation of attacks. A one-class Gaussian Mixture Model is used on top of
these embeddings for the PAD task. The proposed framework introduces a novel
approach to learn a robust PAD system from bonafide and available (known)
attack classes. This is particularly important as collecting bonafide data and
simpler attacks are much easier than collecting a wide variety of expensive
attacks. The proposed system is evaluated on the publicly available WMCA
multi-channel face PAD database, which contains a wide variety of 2D and 3D
attacks. Further, we have performed experiments with MLFP and SiW-M datasets
using RGB channels only. Superior performance in unseen attack protocols shows
the effectiveness of the proposed approach. Software, data, and protocols to
reproduce the results are made available publicly.Comment: 15 page
Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems
[EN] Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ.
The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level
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