50 research outputs found
Evaluation of Biometric Systems
International audienceBiometrics is considered as a promising solution among traditional methods based on "what we own" (such as a key) or "what we know" (such as a password). It is based on "what we are" and "how we behave". Few people know that biometrics have been used for ages for identification or signature purposes. In 1928 for example, fingerprints were used for women clerical employees of Los Angeles police department as depicted in Figure 1. Fingerprints were also already used as a signature for commercial exchanges in Babylon (-3000 before JC). Alphonse Bertillon proposed in 1879 to use anthropometric information for police investigation. Nowadays, all police forces in the world use this kind of information to resolve crimes. The first prototypes of terminals providing an automatic processing of the voice and digital fingerprints have been defined in the middle of the years 1970. Nowadays, biometric authentication systems have many applications [1]: border control, e-commerce, etc. The main benefits of this technology are to provide a better security, and to facilitate the authentication process for a user. Also, it is usually difficult to copy the biometric characteristics of an individual than most of other authentication methods such as passwords. Despite the obvious advantages of biometric systems, their proliferation was not as much as attended. The main drawback is the uncertainty of the verification result. By contrast to password checking, the verification of biometric raw data is subject to errors and represented by a similarity percentage (100% is never reached). Others drawbacks related to vulnerabilities and usability issues exist. In addition, in order to be used in an industrial context, the quality of a biometric system must be precisely quantified. We need a reliable evaluation methodology in order to put into obviousness the benefit of a new biometric system. Moreover, many questions remain: Shall we be confident in this technology? What kind of biometric modalities can be used? What are the trends in this domain? The objective of this chapter is to answer these questions, by presenting an evaluation methodology of biometric systems
BioSecure: white paper for research in biometrics beyond BioSecure
This report is the output of a consultation process of various major stakeholders in the biometric community to identify the future biometrical research issues, an activity which employed not only researchers but representatives from the entire biometrical community, consisting of governments, industry, citizens and academia. It is one of the main efforts of the BioSecure Network of Excellence to define the agenda for future biometrical research, including systems and applications scenarios
Hand Image Feature for Human Identification
This paper presents an algorithm for efficient personal identification using robust hand
features. The feature is extracted from hand boundary points and print of hand palm. The centre of
gravity of the edge map of the hand image is determined to serve as a reference point. Thereafter
City block distances between the reference point and hand boundary points are found. These
distance feature vectors are compared using Euclidean distance measure for effective image
classification. The proposed algorithm will improve personal identification in access control and
attendance recor
Study of Applicability of Virtual Users in Evaluating Multimodal Biometrics
Abstract. A new approach of enlarging fused biometric databases is presented. Fusion strategies based upon matching score are applied on active biometrics verification scenarios. Consistent biometric data of two traits are used in test scenarios of handwriting and speaker verification. The fusion strategies are applied on multimodal biometrics of two different user types. The real users represent two biometric traits captured from one person. The virtual users are considered as the combination of two traits captured from two discrete users. These virtual users are implemented for database enlargement. In order to investigate the impact of these virtual users, test scenarios using three different semantics of handwriting and speech are accomplished. The results of fused handwriting and speech of exclusively real users and additional virtual users are compared and discussed
Hand Contour Recognition In Language Signs Codes Using Shape Based Hand Gestures Methods
The deaf and speech impaired are loosing of hearing ability followed by disability of developing talking skill in everyday communication. Disability of making normal communication makes the deaf and speech impaired being difficult to be accepted by major normal community. Communication used is gesture language, by using hand gesture communication. The weakness of this communication is that misunderstanding and limitation, it’s due to hand gesture is only understood by minor group. To make effective communication in real time, it’s needed two ways communication that can change the code of hand gesture pattern to the texts and sounds that can be understood by other people. In this research, it’s focused on hand gesture recognition using shaped based hand algorithm where this method classifies image based on hand contour using hausdorff and Euclidian distance to determine the similarity between two hands based on the shortest range. The result of this research is recognizing 26 letters gesture, the accuracy of this Gesture is 85%, from different human hands, taken from different session with different lighting condition and different range of camera from image. It also can recognize 70% different hand contour. The different of this research from other researches is the more the objects are, the less the classification of hands size is. Using this method, hands size can be minimized
Verificaciónn de firma y gráficos manuscritos: CaracterÃsticas discriminantes y nuevos escenarios de aplicación biométrica
Tesis doctoral inédita leÃda en la Escuela Politécnica Superior, Departamento de TecnologÃa Electrónica y de las Comunicaciones. Fecha de lectura: Febrero 2015The proliferation of handheld devices such as smartphones and tablets brings a new
scenario for biometric authentication, and in particular to automatic signature verification.
Research on signature verification has been traditionally carried out using signatures acquired
on digitizing tablets or Tablet-PCs.
This PhD Thesis addresses the problem of user authentication on handled devices using
handwritten signatures and graphical passwords based on free-form doodles, as well as the effects
of biometric aging on signatures. The Thesis pretends to analyze: (i) which are the effects
of mobile conditions on signature and doodle verification, (ii) which are the most distinctive
features in mobile conditions, extracted from the pen or fingertip trajectory, (iii) how do different
similarity computation (i.e. matching) algorithms behave with signatures and graphical
passwords captured on mobile conditions, and (iv) what is the impact of aging on signature
features and verification performance.
Two novel datasets have been presented in this Thesis. A database containing free-form
graphical passwords drawn with the fingertip on a smartphone is described. It is the first publicly
available graphical password database to the extent of our knowledge. A dataset containing
signatures from users captured over a period 15 months is also presented, aimed towards the
study of biometric aging.
State-of-the-art local and global matching algorithms are used, namely Hidden Markov Models,
Gaussian Mixture Models, Dynamic Time Warping and distance-based classifiers. A large
proportion of features presented in the research literature is considered in this Thesis.
The experimental contribution of this Thesis is divided in three main topics: signature verification
on handheld devices, the effects of aging on signature verification, and free-form graphical
password-based authentication. First, regarding signature verification in mobile conditions, we
use a database captured both on a handheld device and digitizing tablet in an office-like scenario.
We analyze the discriminative power of both global and local features using discriminant analysis
and feature selection techniques. The effects of the lack of pen-up trajectories on handheld
devices (when the stylus tip is not in contact with the screen) are also studied.
We then analyze the effects of biometric aging on the signature trait. Using three different
matching algorithms, Hidden Markov Models (HMM), Dynamic Time Warping (DTW), and
distance-based classifiers, the impact in verification performance is studied. We also study
the effects of aging on individual users and individual signature features. Template update
techniques are analyzed as a way of mitigating the negative impact of aging.
Regarding graphical passwords, the DooDB graphical password database is first presented.
A statistical analysis is performed comparing the database samples (free-form doodles and simplified
signatures) with handwritten signatures. The sample variability (inter-user, intra-user
and inter-session) is also analyzed, as well as the learning curve for each kind of trait. Benchmark
results are also reported using state of the art classifiers.
Graphical password verification is afterwards studied using features and matching algorithms
from the signature verification state of the art. Feature selection is also performed and the
resulting feature sets are analyzed.
The main contributions of this work can be summarized as follows. A thorough analysis of
individual feature performance has been carried out, both for global and local features and on
signatures acquired using pen tablets and handheld devices. We have found which individual
features are the most robust and which have very low discriminative potential (pen inclination
and pressure among others). It has been found that feature selection increases verification
performance dramatically, from example from ERRs (Equal Error Rates) over 30% using all
available local features, in the case of handheld devices and skilled forgeries, to rates below 20%
after feature selection. We study the impact of the lack of trajectory information when the pen
tip is not in contact with the acquisition device surface (which happens when touchscreens are
used for signature acquisitions), and we have found that the lack of pen-up trajectories negatively
affects verification performance. As an example, the EER for the local system increases from
9.3% to 12.1% against skilled forgeries when pen-up trajectories are not available.
We study the effects of biometric aging on signature verification and study a number of ways
to compensate the observed performance degradation. It is found that aging does not affect
equally all the users in the database and that features related to signature dynamics are more
degraded than static features. Comparing the performance using test signatures from the first
months with the last months, a variable effect of aging on the EER against random forgeries is
observed in the three systems that are evaluated, from 0.0% to 0.5% in the DTW system, from
1.0% to 5.0% in the distance-based system using global features, and from 3.2% to 27.8% in the
HMM system.
A new graphical password database has been acquired and made publicly available. Verification
algorithms for finger-drawn graphical passwords and simplified signatures are compared
and feature analysis is performed. We have found that inter-session variability has a highly
negative impact on verification performance, but this can be mitigated performing feature selection
and applying fusion of different matchers. It has also been found that some feature types
are prevalent in the optimal feature vectors and that classifiers have a very different behavior
against skilled and random forgeries. An EER of 3.4% and 22.1% against random and skilled
forgeries is obtained for free-form doodles, which is a promising performance