784 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
Multispectral Palmprint Recognition Using Textural Features
In order to utilize identification to the best extent, we need robust and
fast algorithms and systems to process the data. Having palmprint as a reliable
and unique characteristic of every person, we extract and use its features
based on its geometry, lines and angles. There are countless ways to define
measures for the recognition task. To analyze a new point of view, we extracted
textural features and used them for palmprint recognition. Co-occurrence matrix
can be used for textural feature extraction. As classifiers, we have used the
minimum distance classifier (MDC) and the weighted majority voting system
(WMV). The proposed method is tested on a well-known multispectral palmprint
dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most
scenarios which outperforms all previous works in multispectral palmprint
recognition.Comment: 5 pages, Published in IEEE Signal Processing in Medicine and Biology
Symposium 201
Three Dimensional Palmprint Recognition using Structured Light Imaging
BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, 29-1 October 2008Palmprint is one of the most unique and stable biometric characteristics. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much 3D depth information is lost in the imaging process. This paper presents a new approach, 3D palmprint recognition, to exploit the 3D structural information of the palm surface. The structured-light imaging is used to acquire the 3D palmprint data, from which the features of Mean Curvature, Gauss Curvature and Surface Type (ST) are extracted. A fast feature matching and score level fusion strategy are then used to classify the input 3D palmprint data. With the established 3D palmprint database, a series of verification and identification experiments are conducted and the results show that 3D palmprint technique can achieve high recognition rate while having high anti-counterfeiting capability.Department of ComputingRefereed conference pape
MobiBits: Multimodal Mobile Biometric Database
This paper presents a novel database comprising representations of five
different biometric characteristics, collected in a mobile, unconstrained or
semi-constrained setting with three different mobile devices, including
characteristics previously unavailable in existing datasets, namely hand
images, thermal hand images, and thermal face images, all acquired with a
mobile, off-the-shelf device. In addition to this collection of data we perform
an extensive set of experiments providing insight on benchmark recognition
performance that can be achieved with these data, carried out with existing
commercial and academic biometric solutions. This is the first known to us
mobile biometric database introducing samples of biometric traits such as
thermal hand images and thermal face images. We hope that this contribution
will make a valuable addition to the already existing databases and enable new
experiments and studies in the field of mobile authentication. The MobiBits
database is made publicly available to the research community at no cost for
non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted
for publication on July 20, 201
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
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