4,075 research outputs found
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
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
A Graph-Theoretical Approach to the Selection of the Minimum Tiling Path from a Physical Map
The problem of computing the minimum tiling path (MTP) from a set of clones arranged in a physical map is a cornerstone of hierarchical (clone-by-clone) genome sequencing projects. We formulate this problem in a graph theoretical framework, and then solve by a combination of minimum hitting set and minimum spanning tree algorithms. The tool implementing this strategy, called FMTP, shows improved performance compared to the widely used software FPC. When we execute FMTP and FPC on the same physical map, the MTP produced by FMTP covers a higher portion of the genome, and uses a smaller number of clones. For instance, on the rice genome the MTP produced by our tool would reduce by about 11 percent the cost of a clone-by-clone sequencing project. Source code, benchmark data sets, and documentation of FMTP are freely available at \u3ehttp://code.google.com/p/fingerprint-based-minimal-tiling-path/ under MIT license
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
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