60 research outputs found
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
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
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
Effect of intravenous tranexamic acid on blood loss and blood transfusion in total knee replacement: a prospective, randomized study in Indian population
Background: Tranexamic acid (TXA) is antifibrinolytic drug which has the property to reduce intraoperative and postoperative bleeding. There are several studies supporting the use of tranexamic acid in total knee replacements (TKR) and few in total hip replacements. Our study was intended to establish the effects of tranexamic acid in minimizing the intra operative and post-operative blood loss in uncomplicated primary total knee replacement.Methods: This was a prospective follow up study conducted in Rajarajeshwari Medical College and Hospital Bangalore, over a period of 14 months from June 2015 to August 2016. A total number of 60 patients who underwent unilateral primary total knee replacement were included for this study. They were randomly divided into 2 groups. Group I patients infused (intravenous) with 20 mg/kg TXA before incision and 3 hours after surgery whereas no TXA was administered in Group II. Total blood loss and transfusion rate were used as outcome. Results: Mean amounts of blood loss were 578 ml in Group 1 and 946 ml in Group 2. There was a decrease in blood loss in TXA groups (p<0.001). Transfusion was required in 6 patients of Group I and 17 patients of Group II (p<0.001). No thromboembolic problem was seen in any patients.Conclusions: Since TXA decrease perioperative blood loss and lessen the need for blood transfusion significantly, without increasing thromboembolic events in TKR. We suggest using intravenous (IV) TXA in TKR.
Does complimentary information from multispectral imaging improve face presentation attack detection?
Presentation Attack Detection (PAD) has been extensively studied,
particularly in the visible spectrum. With the advancement of sensing
technology beyond the visible range, multispectral imaging has gained
significant attention in this direction. We present PAD based on multispectral
images constructed for eight different presentation artifacts resulted from
three different artifact species. In this work, we introduce Face Presentation
Attack Multispectral (FPAMS) database to demonstrate the significance of
employing multispectral imaging. The goal of this work is to study
complementary information that can be combined in two different ways (image
fusion and score fusion) from multispectral imaging to improve the face PAD.
The experimental evaluation results present an extensive qualitative analysis
of 61650 sample multispectral images collected for bonafide and artifacts. The
PAD based on the score fusion and image fusion method presents superior
performance, demonstrating the significance of employing multispectral imaging
to detect presentation artifacts.Comment: Accepted in International IEEE Applied Sensing Conference (IEEE
APSCON) 202
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