484 research outputs found
ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation
Electrocardiograms (ECGs) have shown unique patterns to distinguish between
different subjects and present important advantages compared to other biometric
traits, such as difficulty to counterfeit, liveness detection, and ubiquity.
Also, with the success of Deep Learning technologies, ECG biometric recognition
has received increasing interest in recent years. However, it is not easy to
evaluate the improvements of novel ECG proposed methods, mainly due to the lack
of public data and standard experimental protocols. In this study, we perform
extensive analysis and comparison of different scenarios in ECG biometric
recognition. Both verification and identification tasks are investigated, as
well as single- and multi-session scenarios. Finally, we also perform single-
and multi-lead ECG experiments, considering traditional scenarios using
electrodes in the chest and limbs and current user-friendly wearable devices.
In addition, we present ECGXtractor, a robust Deep Learning technology
trained with an in-house large-scale database and able to operate successfully
across various scenarios and multiple databases. We introduce our proposed
feature extractor, trained with multiple sinus-rhythm heartbeats belonging to
55,967 subjects, and provide a general public benchmark evaluation with
detailed experimental protocol. We evaluate the system performance over four
different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv)
CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14%
and 2.06% in verification, and accuracies of 100% and 96.46% in identification,
respectively in single- and multi-session analysis. We release the source code,
experimental protocol details, and pre-trained models in GitHub to advance in
the field.Comment: 11 pages, 4 figure
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic
This work proposes and analyzes the use of keystroke biometrics for content
de-anonymization. Fake news have become a powerful tool to manipulate public
opinion, especially during major events. In particular, the massive spread of
fake news during the COVID-19 pandemic has forced governments and companies to
fight against missinformation. In this context, the ability to link multiple
accounts or profiles that spread such malicious content on the Internet while
hiding in anonymity would enable proactive identification and blacklisting.
Behavioral biometrics can be powerful tools in this fight. In this work, we
have analyzed how the latest advances in keystroke biometric recognition can
help to link behavioral typing patterns in experiments involving 100,000 users
and more than 1 million typed sequences. Our proposed system is based on
Recurrent Neural Networks adapted to the context of content de-anonymization.
Assuming the challenge to link the typed content of a target user in a pool of
candidate profiles, our results show that keystroke recognition can be used to
reduce the list of candidate profiles by more than 90%. In addition, when
keystroke is combined with auxiliary data (such as location), our system
achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a
background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362
BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation
Mobile behavioral biometrics have become a popular topic of research,
reaching promising results in terms of authentication, exploiting a multimodal
combination of touchscreen and background sensor data. However, there is no way
of knowing whether state-of-the-art classifiers in the literature can
distinguish between the notion of user and device. In this article, we present
a new database, BehavePassDB, structured into separate acquisition sessions and
tasks to mimic the most common aspects of mobile Human-Computer Interaction
(HCI). BehavePassDB is acquired through a dedicated mobile app installed on the
subjects' devices, also including the case of different users on the same
device for evaluation. We propose a standard experimental protocol and
benchmark for the research community to perform a fair comparison of novel
approaches with the state of the art. We propose and evaluate a system based on
Long-Short Term Memory (LSTM) architecture with triplet loss and modality
fusion at score level.Comment: 11 pages, 3 figure
Biometric Signature Verification Using Recurrent Neural Networks
“© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-The art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeriesThis work has been supported by project TEC2015-70627-R MINECO/FEDER and by UAM-CecaBank Project. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC
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