51 research outputs found
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A novel word-independent gesture-typing continuous authentication scheme for mobile devices
In this study, we produce a new continuous authentication scheme for gesture-typing on mobile devices. Our scheme is the first scheme that authenticates gesture-typing interactions in a word-independent format. The scheme relies on groupings of features extracted from the word gesture after it has been reduced to parts common to all gestures. We show that movement sensors are also important in differentiating between users. We describe the feature extraction processes and analyse our proposed feature set. The unique process of our authentication scheme is presented and described. We collect our own gesture typing dataset including data collected during sitting, standing and walking activities for realism. We test our features against state-of-the-art touch-screen interaction features and compare feature extraction times on real mobile devices. Our scheme authenticates users with an equal error rate of 3.58% for a single word-gesture. The equal error rate is reduced to 0.81% when 3 word-gestures are used to authenticate
On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
In this paper, we study the inference of gender, major/minor (computer
science, non-computer science), typing style, age, and height from the typing
patterns collected from 117 individuals in a multi-device environment. The
inference of the first three identifiers was considered as classification
tasks, while the rest as regression tasks. For classification tasks, we
benchmark the performance of six classical machine learning (ML) and four deep
learning (DL) classifiers. On the other hand, for regression tasks, we
evaluated three ML and four DL-based regressors. The overall experiment
consisted of two text-entry (free and fixed) and four device (Desktop, Tablet,
Phone, and Combined) configurations. The best arrangements achieved accuracies
of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor,
respectively, and mean absolute errors of 1.77 years and 2.65 inches for age
and height, respectively. The results are promising considering the variety of
application scenarios that we have listed in this work.Comment: The first two authors contributed equally. The code is available upon
request. Please contact the last autho
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
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 art1. We propose and evaluate a system based on
Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score levelThis project has received funding from the European Unions
Horizon 2020 research and innovation programme under the Marie
Skodowska-Curie grant agreement no. 860315, and from Orange
Labs. R. Tolosana and R. Vera-Rodriguez are also supported by
INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER
Two-Dimensional Dynamic Fusion for Continuous Authentication
Continuous authentication has been widely studied to provide high security
and usability for mobile devices by continuously monitoring and authenticating
users. Recent studies adopt multibiometric fusion for continuous authentication
to provide high accuracy even when some of captured biometric data are of a low
quality. However, existing continuous fusion approaches are resource-heavy as
they rely on all classifiers being activated all the time and may not be
suitable for mobile devices.
In this paper, we propose a new approach to multibiometric continuous
authentication: two-dimensional dynamic fusion. Our key insight is that
multibiometric continuous authentication calculates two-dimensional matching
scores over classifiers and over time. Based on this, we dynamically select a
set of classifiers based on the context in which authentication is taking
place, and fuse matching scores by multi-classifier fusion and multi-sample
fusion. Through experimental evaluation, we show that our approach provides a
better balance between resource usage and accuracy than the existing fusion
methods. In particular, we show that our approach provides higher accuracy than
the existing methods with the same number of score calculations by adopting
multi-sample fusion.Comment: Accepted to IJCB'2
Touch-screen Behavioural Biometrics on Mobile Devices
Robust user verification on mobile devices is one of the top priorities globally from a financial security and privacy viewpoint and has led to biometric verification complementing or replacing PIN and password methods. Research has shown that behavioural biometric methods, with their promise of improved security due to inimitable nature and the lure of unintrusive, implicit, continuous verification, could define the future of privacy and cyber security in an increasingly mobile world. Considering the real-life nature of problems relating to mobility, this study aims to determine the impact of user interaction factors that affect verification performance and usability for behavioural biometric modalities on mobile devices. Building on existing work on biometric performance assessments, it asks: To what extent does the biometric performance remain stable when faced with movements or change of environment, over time and other device related factors influencing usage of mobile devices in real-life applications? Further it seeks to provide answers to: What could further improve the performance for behavioural biometric modalities?
Based on a review of the literature, a series of experiments were executed to collect a dataset consisting of touch dynamics based behavioural data mirroring various real-life usage scenarios of a mobile device. Responses were analysed using various uni-modal and multi-modal frameworks. Analysis demonstrated that existing verification methods using touch modalities of swipes, signatures and keystroke dynamics adapt poorly when faced with a variety of usage scenarios and have challenges related to time persistence. The results indicate that a multi-modal solution does have a positive impact towards improving the verification performance. On this basis, it is recommended to explore alternatives in the form of dynamic, variable thresholds and smarter template selection strategy which hold promise. We believe that the evaluation results presented in this thesis will streamline development of future solutions for improving the security of behavioural-based modalities on mobile biometrics
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