93 research outputs found
Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network
Fingerphoto images captured using a smartphone are successfully used to
verify the individuals that have enabled several applications. This work
presents a novel algorithm for fingerphoto verification using a nested residual
block: Finger-NestNet. The proposed Finger-NestNet architecture is designed
with three consecutive convolution blocks followed by a series of nested
residual blocks to achieve reliable fingerphoto verification. This paper also
presents the interpretability of the proposed method using four different
visualization techniques that can shed light on the critical regions in the
fingerphoto biometrics that can contribute to the reliable verification
performance of the proposed method. Extensive experiments are performed on the
fingerphoto dataset comprised of 196 unique fingers collected from 52 unique
data subjects using an iPhone6S. Experimental results indicate the improved
verification of the proposed method compared to six different existing methods
with EER = 1.15%.Comment: a preprint paper accepted in wacv2023 worksho
Fingervein Verification using Convolutional Multi-Head Attention Network
Biometric verification systems are deployed in various security-based
access-control applications that require user-friendly and reliable person
verification. Among the different biometric characteristics, fingervein
biometrics have been extensively studied owing to their reliable verification
performance. Furthermore, fingervein patterns reside inside the skin and are
not visible outside; therefore, they possess inherent resistance to
presentation attacks and degradation due to external factors. In this paper, we
introduce a novel fingervein verification technique using a convolutional
multihead attention network called VeinAtnNet. The proposed VeinAtnNet is
designed to achieve light weight with a smaller number of learnable parameters
while extracting discriminant information from both normal and enhanced
fingervein images. The proposed VeinAtnNet was trained on the newly constructed
fingervein dataset with 300 unique fingervein patterns that were captured in
multiple sessions to obtain 92 samples per unique fingervein. Extensive
experiments were performed on the newly collected dataset FV-300 and the
publicly available FV-USM and FV-PolyU fingervein dataset. The performance of
the proposed method was compared with five state-of-the-art fingervein
verification systems, indicating the efficacy of the proposed VeinAtnNet.Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer
Vision (WACV), 202
Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.Comment: 29 pages, submitted to ACM computing survey journa
3D Face Morphing Attacks: Generation, Vulnerability and Detection
Face Recognition systems (FRS) have been found to be vulnerable to morphing
attacks, where the morphed face image is generated by blending the face images
from contributory data subjects. This work presents a novel direction for
generating face-morphing attacks in 3D. To this extent, we introduced a novel
approach based on blending 3D face point clouds corresponding to contributory
data subjects. The proposed method generates 3D face morphing by projecting the
input 3D face point clouds onto depth maps and 2D color images, followed by
image blending and wrapping operations performed independently on the color
images and depth maps. We then back-projected the 2D morphing color map and the
depth map to the point cloud using the canonical (fixed) view. Given that the
generated 3D face morphing models will result in holes owing to a single
canonical view, we have proposed a new algorithm for hole filling that will
result in a high-quality 3D face morphing model. Extensive experiments were
conducted on the newly generated 3D face dataset comprising 675 3D scans
corresponding to 41 unique data subjects and a publicly available database
(Facescape) with 100 data subjects. Experiments were performed to benchmark the
vulnerability of the {proposed 3D morph-generation scheme against} automatic
2D, 3D FRS, and human observer analysis. We also presented a quantitative
assessment of the quality of the generated 3D face-morphing models using eight
different quality metrics. Finally, we propose three different 3D face Morphing
Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face
morphing attack detection techniques.Comment: The paper is accepted at IEEE Transactions on Biometrics, Behavior
and Identity Scienc
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
Face manipulation attacks have drawn the attention of biometric researchers
because of their vulnerability to Face Recognition Systems (FRS). This paper
proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based
on the Generative Adversarial Networks (GANs). Given the face images from
contributory data subjects, the proposed CFIA method will independently
generate the segmented facial attributes, then blend them using transparent
masks to generate the CFIA samples. { The primary motivation for CFIA is to
utilize deep learning to generate facial attribute-based composite attacks,
which has been explored relatively less in the current literature.} We generate
different combinations of facial attributes resulting in unique CFIA
samples for each pair of contributory data subjects. Extensive experiments are
carried out on our newly generated CFIA dataset consisting of 1000 unique
identities with 2000 bona fide samples and 14000 CFIA samples, thus resulting
in an overall 16000 face image samples. We perform a sequence of experiments to
benchmark the vulnerability of CFIA to automatic FRS (based on both
deep-learning and commercial-off-the-shelf (COTS). We introduced a new metric
named Generalized Morphing Attack Potential (GMAP) to benchmark the
vulnerability effectively. Additional experiments are performed to compute the
perceptual quality of the generated CFIA samples. Finally, the CFIA detection
performance is presented using three different Face Morphing Attack Detection
(MAD) algorithms. The proposed CFIA method indicates good perceptual quality
based on the obtained results. Further, { FRS is vulnerable to CFIA} (much
higher than SOTA), making it difficult to detect by human observers and
automatic detection algorithms. Lastly, we performed experiments to detect the
CFIA samples using three different detection techniques automatically
Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance
Face Recognition Systems (FRS) are vulnerable to various attacks performed
directly and indirectly. Among these attacks, face morphing attacks are highly
potential in deceiving automatic FRS and human observers and indicate a severe
security threat, especially in the border control scenario. This work presents
a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic
Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD)
algorithm based on the spherical interpolation and hierarchical fusion of deep
features computed from six different pre-trained deep Convolutional Neural
Networks (CNNs). Extensive experiments are carried out on the newly generated
face morphing dataset (SCFace-Morph) based on the publicly available SCFace
dataset by considering the real-life scenario of Automatic Border Control (ABC)
gates. Experimental protocols are designed to benchmark the proposed and
state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and
capture distances. Obtained results have indicated the superior performance of
the proposed D-MAD method compared to the existing methods.Comment: The paper is accepted at the International Joint Conference on
Biometrics (IJCB) 202
Sound-Print: Generalised Face Presentation Attack Detection using Deep Representation of Sound Echoes
Facial biometrics are widely deployed in smartphone-based applications
because of their usability and increased verification accuracy in unconstrained
scenarios. The evolving applications of smartphone-based facial recognition
have also increased Presentation Attacks (PAs), where an attacker can present a
Presentation Attack Instrument (PAI) to maliciously gain access to the
application. Because the materials used to generate PAI are not deterministic,
the detection of unknown presentation attacks is challenging. In this paper, we
present an acoustic echo-based face Presentation Attack Detection (PAD) on a
smartphone in which the PAs are detected based on the reflection profiles of
the transmitted signal. We propose a novel transmission signal based on the
wide pulse that allows us to model the background noise before transmitting the
signal and increase the Signal-to-Noise Ratio (SNR). The received signal
reflections were processed to remove background noise and accurately represent
reflection characteristics. The reflection profiles of the bona fide and PAs
are different owing to the different reflection characteristics of the human
skin and artefact materials. Extensive experiments are presented using the
newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured
from bona fide and four different types of PAIs, including print (two types),
display, and silicone face-mask attacks. The obtained results indicate the
robustness of the proposed method for detecting unknown face presentation
attacks.Comment: Accepted in IJCB 202
Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network
Face Recognition System (FRS) are shown to be vulnerable to morphed images of
newborns. Detecting morphing attacks stemming from face images of newborn is
important to avoid unwanted consequences, both for security and society. In
this paper, we present a new reference-based/Differential Morphing Attack
Detection (MAD) method to detect newborn morphing images using Wavelet
Scattering Network (WSN). We propose a two-layer WSN with 250 250
pixels and six rotations of wavelets per layer, resulting in 577 paths. The
proposed approach is validated on a dataset of 852 bona fide images and 2460
morphing images constructed using face images of 42 unique newborns. The
obtained results indicate a gain of over 10\% in detection accuracy over other
existing D-MAD techniques.Comment: accepted in 5th International Conference on Bio-engineering for Smart
Technologies (BIO-SMART 2023
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