Tesis doctoral inédita leída en la Escuela Politécnica Superior, Departamento de Tecnología 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