164 research outputs found
Liquid-Liquid Equilibrium of Poly(Ethylene Glycol) 6000 + Sodium Succinate + Water System at Different Temperatures
Phase diagrams and the compositions of coexisting phases of poly(ethylene glycol) (PEG) 6000 + sodium succinate + water system have been determined experimentally at 298.15, 308.15, and 318.15 K. The effects of temperature on the binodal curve and tie lines have been studied. The binodal curves were successfully fitted to a nonlinear equation relating the concentrations of PEG 6000 and sodium succinate, and the coefficients were estimated for the formentioned systems (low AARD, high R2, and low SD). Tie-line compositions were estimated and correlated using Othmer-Tobias and Bancroft equations, and the parameters were reported. The effect of temperature on the phase-forming ability has been studied by fitting the binodal data to a Setschenow-type equation for each temperature. The effective excluded volume (EEV) values were also calculated from the binodal data, and it was found out that the values increased with an increase in the temperature. Furthermore, the effect of MW of PEG on the phase diagram has been studied and verified
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
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
Face morphing attacks have raised critical concerns as they demonstrate a new
vulnerability of Face Recognition Systems (FRS), which are widely deployed in
border control applications. The face morphing process uses the images from
multiple data subjects and performs an image blending operation to generate a
morphed image of high quality. The generated morphed image exhibits similar
visual characteristics corresponding to the biometric characteristics of the
data subjects that contributed to the composite image and thus making it
difficult for both humans and FRS, to detect such attacks. In this paper, we
report a systematic investigation on the vulnerability of the
Commercial-Off-The-Shelf (COTS) FRS when morphed images under the influence of
ageing are presented. To this extent, we have introduced a new morphed face
dataset with ageing derived from the publicly available MORPH II face dataset,
which we refer to as MorphAge dataset. The dataset has two bins based on age
intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects
with the age variation of 1 year to 2 years while the MorphAge-II dataset
consists of 516 data subjects whose age intervals are from 2 years to 5 years.
To effectively evaluate the vulnerability for morphing attacks, we also
introduce a new evaluation metric, namely the Fully Mated Morphed Presentation
Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic
scenario. Extensive experiments are carried out by using two different COTS FRS
(COTS I - Cognitec and COTS II - Neurotechnology) to quantify the vulnerability
with ageing. Further, we also evaluate five different Morph Attack Detection
(MAD) techniques to benchmark their detection performance with ageing.Comment: Accepted in IJCB 202
On the Applicability of Synthetic Data for Face Recognition
Face verification has come into increasing focus in various applications
including the European Entry/Exit System, which integrates face recognition
mechanisms. At the same time, the rapid advancement of biometric authentication
requires extensive performance tests in order to inhibit the discriminatory
treatment of travellers due to their demographic background. However, the use
of face images collected as part of border controls is restricted by the
European General Data Protection Law to be processed for no other reason than
its original purpose. Therefore, this paper investigates the suitability of
synthetic face images generated with StyleGAN and StyleGAN2 to compensate for
the urgent lack of publicly available large-scale test data. Specifically, two
deep learning-based (SER-FIQ, FaceQnet v1) and one standard-based (ISO/IEC TR
29794-5) face image quality assessment algorithm is utilized to compare the
applicability of synthetic face images compared to real face images extracted
from the FRGC dataset. Finally, based on the analysis of impostor score
distributions and utility score distributions, our experiments reveal
negligible differences between StyleGAN vs. StyleGAN2, and further also minor
discrepancies compared to real face images
Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors
Enabling highly secure applications (such as border crossing) with face
recognition requires extensive biometric performance tests through large scale
data. However, using real face images raises concerns about privacy as the laws
do not allow the images to be used for other purposes than originally intended.
Using representative and subsets of face data can also lead to unwanted
demographic biases and cause an imbalance in datasets. One possible solution to
overcome these issues is to replace real face images with synthetically
generated samples. While generating synthetic images has benefited from recent
advancements in computer vision, generating multiple samples of the same
synthetic identity resembling real-world variations is still unaddressed, i.e.,
mated samples. This work proposes a non-deterministic method for generating
mated face images by exploiting the well-structured latent space of StyleGAN.
Mated samples are generated by manipulating latent vectors, and more precisely,
we exploit Principal Component Analysis (PCA) to define semantically meaningful
directions in the latent space and control the similarity between the original
and the mated samples using a pre-trained face recognition system. We create a
new dataset of synthetic face images (SymFace) consisting of 77,034 samples
including 25,919 synthetic IDs. Through our analysis using well-established
face image quality metrics, we demonstrate the differences in the biometric
quality of synthetic samples mimicking characteristics of real biometric data.
The analysis and results thereof indicate the use of synthetic samples created
using the proposed approach as a viable alternative to replacing real biometric
data
MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
Face morphing attacks target to circumvent Face Recognition Systems (FRS) by
employing face images derived from multiple data subjects (e.g., accomplices
and malicious actors). Morphed images can be verified against contributing data
subjects with a reasonable success rate, given they have a high degree of
facial resemblance. The success of morphing attacks is directly dependent on
the quality of the generated morph images. We present a new approach for
generating strong attacks extending our earlier framework for generating face
morphs. We present a new approach using an Identity Prior Driven Generative
Adversarial Network, which we refer to as MIPGAN (Morphing through Identity
Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a
newly formulated loss function exploiting perceptual quality and identity
factor to generate a high quality morphed facial image with minimal artefacts
and with high resolution. We demonstrate the proposed approach's applicability
to generate strong morphing attacks by evaluating its vulnerability against
both commercial and deep learning based Face Recognition System (FRS) and
demonstrate the success rate of attacks. Extensive experiments are carried out
to assess the FRS's vulnerability against the proposed morphed face generation
technique on three types of data such as digital images, re-digitized (printed
and scanned) images, and compressed images after re-digitization from newly
generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the
proposed approach of morph generation poses a high threat to FRS.Comment: Revised version. Submitted to IEEE T-BIOM 202
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
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