372 research outputs found
Score Normalization for Keystroke Dynamics Biometrics
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. A. Morales, E. Luna-Garcia, J. Fierrez and J. Ortega-Garcia, "Score normalization for keystroke dynamics biometrics," Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 223-228. doi: 10.1109/CCST.2015.7389686This paper analyzes score normalization for keystroke
dynamics authentication systems. Previous studies have shown
that the performance of behavioral biometric recognition systems
(e.g. voice and signature) can be largely improved with score
normalization and target-dependent techniques. The main
objective of this work is twofold: i) to analyze the effects of
different thresholding techniques in 4 different keystroke
dynamics recognition systems for real operational scenarios; and
ii) to improve the performance of keystroke dynamics on the
basis of target-dependent score normalization techniques. The
experiments included in this work are worked out over the
keystroke pattern of 114 users from two different publicly
available databases. The experiments show that there is large
room for improvements in keystroke dynamic systems. The
results suggest that score normalization techniques can be used to
improve the performance of keystroke dynamics systems in more
than 20%. These results encourage researchers to explore this
research line to further improve the performance of these
systems in real operational environments.A.M. is supported by a post-doctoral Juan de la Cierva contract by the Spanish MECD (JCI-2012-12357). This work has been partially supported by projects: Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM Telefónica
Genetic Programming for Multibiometrics
Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined together (multiple captures of the same biometric modality, multiple
feature extraction algorithms, multiple biometric modalities...). In this
paper, we are interested in score level fusion functions application (i.e., we
use a multibiometric authentication scheme which accept or deny the claimant
for using an application). In the state of the art, the weighted sum of scores
(which is a linear classifier) and the use of an SVM (which is a non linear
classifier) provided by different biometric systems provide one of the best
performances. We present a new method based on the use of genetic programming
giving similar or better performances (depending on the complexity of the
database). We derive a score fusion function by assembling some classical
primitives functions (+, *, -, ...). We have validated the proposed method on
three significant biometric benchmark datasets from the state of the art
Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis
Most keystroke dynamics studies have been evaluated using a specific kind of
dataset in which users type an imposed login and password. Moreover, these
studies are optimistics since most of them use different acquisition protocols,
private datasets, controlled environment, etc. In order to enhance the accuracy
of keystroke dynamics' performance, the main contribution of this paper is
twofold. First, we provide a new kind of dataset in which users have typed both
an imposed and a chosen pairs of logins and passwords. In addition, the
keystroke dynamics samples are collected in a web-based uncontrolled
environment (OS, keyboards, browser, etc.). Such kind of dataset is important
since it provides us more realistic results of keystroke dynamics' performance
in comparison to the literature (controlled environment, etc.). Second, we
present a statistical analysis of well known assertions such as the
relationship between performance and password size, impact of fusion schemes on
system overall performance, and others such as the relationship between
performance and entropy. We put into obviousness in this paper some new results
on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding
and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012
Fast computation of the performance evaluation of biometric systems: application to multibiometric
The performance evaluation of biometric systems is a crucial step when
designing and evaluating such systems. The evaluation process uses the Equal
Error Rate (EER) metric proposed by the International Organization for
Standardization (ISO/IEC). The EER metric is a powerful metric which allows
easily comparing and evaluating biometric systems. However, the computation
time of the EER is, most of the time, very intensive. In this paper, we propose
a fast method which computes an approximated value of the EER. We illustrate
the benefit of the proposed method on two applications: the computing of non
parametric confidence intervals and the use of genetic algorithms to compute
the parameters of fusion functions. Experimental results show the superiority
of the proposed EER approximation method in term of computing time, and the
interest of its use to reduce the learning of parameters with genetic
algorithms. The proposed method opens new perspectives for the development of
secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012
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
Towards Engineering Reliable Keystroke Biometrics Systems
In this thesis, we argue that most of the work in the literature on behavioural-based biometric systems using AI and machine learning is immature and unreliable. Our analysis and experimental results show that designing reliable behavioural-based biometric systems requires a systematic and complicated process. We first discuss the limitation in existing work and the use of conventional machine learning methods. We use the biometric zoos theory to demonstrate the challenge of designing reliable behavioural-based biometric systems. Then, we outline the common problems in engineering reliable biometric systems. In particular, we focus on the need for novelty detection machine learning models and adaptive machine learning algorithms. We provide a systematic approach to design and build reliable behavioural-based biometric systems. In our study, we apply the proposed approach to keystroke dynamics. Keystroke dynamics is behavioural-based biometric that identify individuals by measuring their unique typing behaviours on physical or soft keyboards. Our study shows that it is possible to design reliable behavioral-based biometrics and address the gaps in the literature
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