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Section 4 presents our proposed PAD approach. The
experimental evaluation is described in Section 5.
Section 6 concludes this paper and gives an outlook
on futurework.
2.RelatedWork
Finger- and hand-veins have been shown to be
susceptible to spoofing [26, 24]. PAD approaches
help indetectingpresentationattacksandcanbecat-
egorised into liveness-based (rely on signs of vital-
ity, e.g. capturing the heartbeat), motion-based (ana-
lysemovementsduring thecapturingprocessand try
to detect unnatural ones) and texture-based meth-
ods (detect and analyse textural artefacts present in
the image). While the first two categories require a
videoorasequenceofconsecutive images tobecap-
tured, texture-basedmethodscanbeapplied tosingle
images. One liveness based approach is presented
in [19], which applies motion magnification tech-
niques. The majority of the proposed PAD schemes
are texture-based ones, e.g. a Fourier, Haar and
Daubechies wavelet transform based one [16], ex-
ploiting differences in the bandwidth of vertical en-
ergy signals. A binarised statistical image features
based one and some others based on Riesz trans-
form, localbinarypatterns (LBP), localphasequant-
isation and Weber local descriptors are presented
in [25]. Another approach [23] uses a windowed
dynamic mode decomposition (W-DMD) to detect
spoofed finger vein images. Even baseline LBP [20]
and some LBP variants and extensions of LBP [10]
provedtobeeffectivefor thetaskoffingerveinPAD.
Several other approaches are utilising image quality
assessment methods (IQA), e.g. [15] and [1] which
detection accuracy turns out to be highly dependent
on the particular dataset. In [22] the authors showed
that the classification accuracy can be improved by
incorporating natural scene statistics (NSS) [13]. A
very different approach for PAD detection is to use
a photo-response non-uniformity (PRNU) technique
to differentiate PA data from genuine samples [12].
Furthermore, a CNN-based approach has been pro-
posed in [17].
3.PresentationAttackApproaches
Capturing the vein pattern using an appropriate
capturing device forms the basis of vein recognition
ingeneralandfinger-andhand-veinPAevaluationin
particular. Therefore, we utilise the PLUSVein fin-
gerveinscanner [7]andthePLUShandveinscanner (a) Genuine (b) Post-processed (c) Recaptured
(a) Genuine (b) Post-processed (c) Recaptured
Figure1.HandveinPAartefactsfor950nmreflectedlight
illumination captured with the PLUS hand vein scanner
[8]: genuine image (a), post-processed image for printing
(b)and re-acquired printed image (c).
[8] as capturing devices to prepare our finger- and
hand-vein spoofing artefacts as well as for recaptur-
ing the artefacts. The interested reader is referred
to the authors original publications for more details
about those capturing devices. In the following, the
productionof thehandandfingerveinspoofingarte-
facts is described. These spoofing artefacts are then
again presented to the capturing devices mentioned
above.
3.1.HandVeinSpoofingArtefacts
The hand vein capturing device is used to acquire
reflected light images in two different wavelengths
(850 and 950 nm). Since printouts of finger vein
patterns have shown to yield successful presentation
attacks [26], we decided for this approach as an at-
tackscenario for thehandveinrecognitionsystemas
well. Our spoofing attack samples are derived from
samples contained in the publicly available PRO-
TECTVein dataset, which is part of the PROTECT
MultimodalBiometricDatabase [21].
Thehandveinspoofingattacksamplesarecreated
byfirstselecting100imagesbasedonthevisibilityof
the vein pattern (5 dorsal and 5 palmar for one hand
of 10 users). Afterwards, a region of interest (ROI)
is manually cropped from the images. These ROIs
arethenpost-processedusingaContrastLimitedAd-
aptive Histogram Equalisation (CLAHE) and Gauss
filtering, toenhance thevisibilityof thevascularpat-
tern and remove the skin texture and hair to even-
tually obtain smooth images. Afterwards, the post-
processed images are scaled to approximately match
66
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik