4,101 research outputs found
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Designing an end-to-end deep learning network to match the biometric features
with limited training samples is an extremely challenging task. To address this
problem, we propose a new way to design an end-to-end deep CNN framework i.e.,
PVSNet that works in two major steps: first, an encoder-decoder network is used
to learn generative domain-specific features followed by a Siamese network in
which convolutional layers are pre-trained in an unsupervised fashion as an
autoencoder. The proposed model is trained via triplet loss function that is
adjusted for learning feature embeddings in a way that minimizes the distance
between embedding-pairs from the same subject and maximizes the distance with
those from different subjects, with a margin. In particular, a triplet Siamese
matching network using an adaptive margin based hard negative mining has been
suggested. The hyper-parameters associated with the training strategy, like the
adaptive margin, have been tuned to make the learning more effective on
biometric datasets. In extensive experimentation, the proposed network
outperforms most of the existing deep learning solutions on three type of
typical vein datasets which clearly demonstrates the effectiveness of our
proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security
and Behavior Analysis (ISBA), 2019, Hyderabad, Indi
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics
Biometric systems have to address many requirements, such as large population
coverage, demographic diversity, varied deployment environment, as well as
practical aspects like performance and spoofing attacks. Traditional unimodal
biometric systems do not fully meet the aforementioned requirements making them
vulnerable and susceptible to different types of attacks. In response to that,
modern biometric systems combine multiple biometric modalities at different
fusion levels. The fused score is decisive to classify an unknown user as a
genuine or impostor. In this paper, we evaluate combinations of score
normalization and fusion techniques using two modalities (fingerprint and
finger-vein) with the goal of identifying which one achieves better improvement
rate over traditional unimodal biometric systems. The individual scores
obtained from finger-veins and fingerprints are combined at score level using
three score normalization techniques (min-max, z-score, hyperbolic tangent) and
four score fusion approaches (minimum score, maximum score, simple sum, user
weighting). The experimental results proved that the combination of hyperbolic
tangent score normalization technique with the simple sum fusion approach
achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201
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
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
Bimodal Biometric Verification Mechanism using fingerprint and face images(BBVMFF)
An increased demand of biometric authentication coupled with automation of systems is observed in the recent times. Generally biometric recognition systems currently used consider only a single biometric characteristic for verification or authentication. Researchers have proved the inefficiencies in unimodal biometric systems and propagated the adoption of multimodal biometric systems for verification. This paper introduces Bi-modal Biometric Verification Mechanism using Fingerprint and Face (BBVMFF). The BBVMFF considers the frontal face and fingerprint biometric characteristics of users for verification. The BBVMFF Considers both the Gabor phase and magnitude features as biometric trait definitions and simple lightweight feature level fusion algorithm. The fusion algorithm proposed enables the applicability of the proposed BBVMFF in unimodal and Bi-modal modes proved by the experimental results presented
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
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