16 research outputs found
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Biometric recognition, or simply biometrics, is the use of biological
attributes such as face, fingerprints or iris in order to recognize an
individual in an automated manner. A key application of biometrics is
authentication; i.e., using said biological attributes to provide access by
verifying the claimed identity of an individual. This paper presents a
framework for Biometrics-as-a-Service (BaaS) that performs biometric matching
operations in the cloud, while relying on simple and ubiquitous consumer
devices such as smartphones. Further, the framework promotes innovation by
providing interfaces for a plurality of software developers to upload their
matching algorithms to the cloud. When a biometric authentication request is
submitted, the system uses a criteria to automatically select an appropriate
matching algorithm. Every time a particular algorithm is selected, the
corresponding developer is rendered a micropayment. This creates an innovative
and competitive ecosystem that benefits both software developers and the
consumers. As a case study, we have implemented the following: (a) an ocular
recognition system using a mobile web interface providing user access to a
biometric authentication service, and (b) a Linux-based virtual machine
environment used by software developers for algorithm development and
submission
A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis
Convolutional neural networks (CNNs) have
emerged as the most popular classification models in biometrics
research. Under the discriminative paradigm of pattern
recognition, CNNs are used typically in one of two ways: 1)
verification mode (”are samples from the same person?”), where
pairs of images are provided to the network to distinguish
between genuine and impostor instances; and 2) identification
mode (”whom is this sample from?”), where appropriate feature
representations that map images to identities are found. This
paper postulates a novel mode for using CNNs in biometric
identification, by learning models that answer to the question ”is
the query’s identity among this set?”. The insight is a reminiscence
of the classical Mastermind game: by iteratively analysing the
network responses when multiple random samples of k gallery
elements are compared to the query, we obtain weakly correlated
matching scores that - altogether - provide solid cues to infer
the most likely identity. In this setting, identification is regarded
as a variable selection and regularization problem, with sparse
linear regression techniques being used to infer the matching
probability with respect to each gallery identity. As main strength,
this strategy is highly robust to outlier matching scores, which
are known to be a primary error source in biometric recognition.
Our experiments were carried out in full versions of two
well known irises near-infrared (CASIA-IrisV4-Thousand) and
periocular visible wavelength (UBIRIS.v2) datasets, and confirm
that recognition performance can be solidly boosted-up by the
proposed algorithm, when compared to the traditional working
modes of CNNs in biometrics.info:eu-repo/semantics/publishedVersio
Applying Machine Learning to enhance payments systems security
Ph. D. Thesis.During the last two decades, the economic losses because fraudulent card payment transactions have tripled. The significant percentage of losses is because of fraud on e-commerce
transactions. Nowadays, there is a clear trend to use more and more mobile devices to make
electronic purchases, and it is estimated that this trend will continue in the coming years.
In the card payment scheme, big financial institutions process millions of transactions every
day; thus, they can model the processed transactions to predict fraud. On the other hand,
merchants process a much lower number of transactions, but they have access to valuable
information that they can collect from the devices that users utilise during the transaction.
In this thesis, we propose a series of measures to enhance the security of these two scenarios
based on past transactional data and information collected from the users’ device. Most of
the approaches proposed so far to model processed transactions were based on supervised
Machine Learning techniques. We propose a fraud detection system for card payments based
on an unsupervised machine learning technique; thus, the system may be able to recognise
new patterns of fraud.
On the other hand, we are looking far ahead, and because of the increment of use of mobile
devices to conduct payments, we propose a series of measures to enhance the security of the
mobile payment system. We have proposed a user identification and verification systems
for smartphones. We base the identification and verification systems on motion data, so the
systems will not require any explicit action from users
Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics
Title from PDF of title page viewed March 4, 2021Dissertation advisors: Reza Derakhshani and Cory BeardVitaIncludes bibliographical references (page 137-149)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020Ocular biometrics uses physical traits from eye regions such as iris, conjunctival vasculature, and periocular for recognizing the person. Ocular biometrics has gained popularity amongst research and industry alike for its identification capabilities, security, and simplicity in the acquisition, even using a mobile phone's selfie camera.
With the rapid advancement in hardware and deep learning technologies, better performances have been obtained using Convolutional Neural Networks(CNN) for feature extraction and person recognition. Most of the early works proposed using large CNNs for ocular recognition in subject-dependent evaluation, where the subjects overlap between the training and testing set. This is difficult to scale for the large population as the CNN model needs to be re-trained every time a new subject is enrolled in the database. Also, many of the proposed CNN models are large, which renders them memory intensive and computationally costly to deploy on a mobile device.
In this work, we propose CNN based robust subject-independent feature extraction for ocular biometric recognition, which is memory and computation efficient. We evaluated our proposed method on various ocular biometric datasets in the subject-independent, cross-dataset, and cross-illumination protocols.Introduction -- Previous Work -- Calculating CNN Models Computational Efficiency -- Case Study of Deep Learning Models in Ocular Biometrics -- OcularNet Model -- OcularNet-v2: Self-learned ROI detection with deep features -- LOD-V: Large Ocular Biometrics Dataset in Visible Spectrum -- Conclusion and Future Work -- Appendix A. Supplementary Materials for Chapter 4 -- Appendix B. Supplementary Materials for Chapter 5 -- Appendix C.Supplementary Materials for Chapter 6 -- Appendix D. Supplementary Materials for Chapter 7xxii, 150 page
Consumer-facing technology fraud : economics, attack methods and potential solutions
The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks