196 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
Handbook of Vascular Biometrics
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers
Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition
Despite the high biometric performance, finger-vein recognition systems are
vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we
present a new and robust approach for detecting presentation attacks on
finger-vein biometric systems exploiting the 3D Shape (normal-map) and material
properties (diffuse-map) of the finger. Observing the normal-map and
diffuse-map exhibiting enhanced textural differences in comparison with the
original finger-vein image, especially in the presence of varying illumination
intensity, we propose to employ textural feature-descriptors on both of them
independently. The features are subsequently used to compute a separating
hyper-plane using Support Vector Machine (SVM) classifiers for the features
computed from normal-maps and diffuse-maps independently. Given the scores from
each classifier for normal-map and diffuse-map, we propose sum-rule based score
level fusion to make detection of such presentation attack more robust. To this
end, we construct a new database of finger-vein images acquired using a custom
capture device with three inbuilt illuminations and validate the applicability
of the proposed approach. The newly collected database consists of 936 images,
which corresponds to 468 bona fide images and 468 artefact images. We establish
the superiority of the proposed approach by benchmarking it with classical
textural feature-descriptor applied directly on finger-vein images. The
proposed approach outperforms the classical approaches by providing the Attack
Presentation Classification Error Rate (APCER) & Bona fide Presentation
Classification Error Rate (BPCER) of 0% compared to comparable traditional
methods.Comment: This work was accepted in The 15th International Conference on SIGNAL
IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS, 201
On the Generalisation Capabilities of Fingerprint Presentation Attack Detection Methods in the Short Wave Infrared Domain
Nowadays, fingerprint-based biometric recognition systems are becoming
increasingly popular. However, in spite of their numerous advantages, biometric
capture devices are usually exposed to the public and thus vulnerable to
presentation attacks (PAs). Therefore, presentation attack detection (PAD)
methods are of utmost importance in order to distinguish between bona fide and
attack presentations. Due to the nearly unlimited possibilities to create new
presentation attack instruments (PAIs), unknown attacks are a threat to
existing PAD algorithms. This fact motivates research on generalisation
capabilities in order to find PAD methods that are resilient to new attacks. In
this context, we evaluate the generalisability of multiple PAD algorithms on a
dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI
species. The PAD data is captured in the short wave infrared domain and the
results discuss the advantages and drawbacks of this PAD technique regarding
unknown attacks
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