120 research outputs found
One-handed keystroke biometric identification competition
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. J. V. Monaco, G. Perez, C. C. Tappert, P. Bours, S. Modal, S. Rajkumar, A. Morales, J. Fierrez, and J. Ortega-Garcia, "One-handed Keystroke Biometric Identification Competition", in International Conference on Biometrics, ICB 2015, 58-64This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.The authors would like to acknowledge the support from
the National Science Foundation under Grant No. 1241585.
Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and
do not necessarily reflect the views of the National Science
Foundation or the US government
KBOC: Keystroke Biometrics OnGoing Competition
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 worksThis paper presents the first Keystroke Biometrics Ongoing
evaluation platform and a Competition (KBOC) organized
to promote reproducible research and establish a baseline
in person authentication using keystroke biometrics. The
ongoing evaluation tool has been developed using the
BEAT platform and includes keystroke sequences (fixedtext)
from 300 users acquired in 4 different sessions. In
addition, the results of a parallel offline competition based
on the same data and evaluation protocol are presented.
The results reported have achieved EERs as low as 5.32%,
which represent a challenging baseline for keystroke
recognition technologies to be evaluated on the new
publicly available KBOC benchmarkA.M. and M. G.-B. are supported by a JdC contract (JCI-2012-
12357) and a FPU Fellowship from Spanish MINECO and MCD,
respectively. J.M. and J.C. are supported by CAPES and CNPq
(grant 304853/2015-1). This work was partially funded by the
projects: CogniMetrics (TEC2015-70627-R) from MINECO
FEDER and BEAT (FP7-SEC-284989) from E
Keystroke Biometrics Ongoing Competition
This paper presents the first Keystroke Biometrics Ongoing Competition (KBOC) organized to establish a reproducible baseline in person authentication using keystroke biometrics. The competition has been developed using the BEAT platform and includes one of the largest keystroke databases publicly available based on a fixed text scenario. The database includes genuine and attacker keystroke sequences from 300 users acquired in 4 different sessions distributed in a four month time span. The sequences correspond to the user's name and surname and therefore each user comprises an individual and personal sequence. As baseline for KBOC we report the results of 31 different algorithms evaluated according to performance and robustness. The systems have achieved EERs as low as 5.32% and high robustness against multisession variability with drop of performances lower than 1% for probes separated by months. The entire database is publicly available at the competition website
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
Investigación reproducible: uso de la plataforma BEAT para la evaluación tecnológica de algoritmos de reconocimiento biométrico
La reproducibilidad es un tema de gran preocupación dentro de la comunidad
científica. Gran cantidad de los artículos científicos que se publican en la actualidad
carecen del suficiente detalle, código o datos para garantizar la reproducibilidad de los
mismos por otros investigadores. En este trabajo se presenta una herramienta de
evaluación tecnológica desarrollada a partir de la plataforma Biometric Evaluation and
Testing (BEAT) orientada a fomentar la experimentación reproducible dentro del área del
reconocimiento biométrico. Para ello, se ha propuesto una competición internacional
(Keystroke Biometric Ongoing Competition - KBOC) centrada en la evaluación de sistemas
de reconocimiento de usuarios a través de dinámica de tecleo.
Los principales objetivos de esta competición son impulsar la investigación en
dinámica de tecleo (atraer a nuevos investigadores), impulsar la plataforma BEAT y ser
una base común donde comparar los sistemas biométricos.
La competición incluye una de la mayores base de datos en dinámica de tecleo con
más de 300 usuarios y 4 sesiones diferentes (escriben nombre y apellido).
Hay dos maneras de participar en KBOC (misma base de datos para las dos):
Competición Ongoing (permanecerá activa por tiempo indefinido), desarrollada en la
plataforma BEAT y competición Offline que sirve de referencia para la competición
Ongoing.
En este TFG se introduce la plataforma (los recursos creados para la competición,
etc.), se detallan las características de la competición y se presentan los resultados ongoing
(experimentos facilitados por la competición) y los resultados y sistemas de los
participantes en la competición offline.The reproducibility of the research is a worrisome topic among the scientific
community.
In this work, a tool developed under BEAT platform for technological evaluation is
presented, which fosters the reproducible research inside the biometric field. In order to do
that, an international competition (Keystroke Biometric Ongoing Competition) focused on
the assessment of the recognition systems has been proposed by means of keystrokes
dynamic.
The ongoing evaluation tool has been developed using the BEAT platform and
includes keystroke sequences (fixed-text) from 300 users acquired in 4 different sessions
under realistic conditions.
In this work, we introduce the platform, we detail the features of the competition and
we present the results. In addition, the results of a parallel offline competition based on the
same data and evaluation protocol are presented. The results reported have achieved EERs
as low as 5.32%, which represent a challenging baseline for keystroke recognition
technologies to be evaluated on the new publicly available KBOC platform
Identification of User Behavioural Biometrics for Authentication using Keystroke Dynamics and Machine Learning
This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively. In addition, mRMR feature selection has been applied to increase the classification performance metrics and to confirm the identity of the users based on the way they access computing devices. From the results, we conclude that device information and touch pressure effectively contribute to identifying each user. Out of them, features that contain device information are responsible for increasing the performance metrics of the system by adding a token-based authentication layer. Based upon the results, random forest yields better classification results for this dataset. The research will contribute significantly to the field of cyber-security by forming a robust authentication system using machine learning algorithms
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