Identity verification based on handwritten signatures with haptic information using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{journals/tomccap/AlsulaimanSVE13,
  author =       "Fawaz A. Alsulaiman and Nizar Sakr and 
                 Julio J. Valdes and Abdulmotaleb El-Saddik",
  title =        "Identity verification based on handwritten signatures
                 with haptic information using genetic programming",
  journal =      "ACM Transactions on Multimedia Computing,
                 Communications, and Applications",
  year =         "2013",
  volume =       "9",
  number =       "2",
  pages =        "11:1--11:21",
  articleno =    "11",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Biometrics,
                 Haptics, classification, user verification",
  acmid =        "2457453",
  publisher =    "ACM",
  ISSN =         "1551-6857",
  bibdate =      "2013-06-05",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/tomccap/tomccap9.html#AlsulaimanSVE13",
  URL =          "http://doi.acm.org/http://dx.doi.org/10.1145/2457450.2457453",
  DOI =          "doi:10.1145/2457450.2457453",
  size =         "21 pages",
  abstract =     "In this article, haptic-based handwritten signature
                 verification using Genetic Programming (GP)
                 classification is presented. A comparison of GP-based
                 classification with classical classifiers including
                 support vector machine, k-nearest neighbours, naive
                 Bayes, and random forest is conducted. In addition, the
                 use of GP in discovering small knowledge-preserving
                 subsets of features in high-dimensional datasets of
                 haptic-based signatures is investigated and several
                 approaches are explored. Subsets of features extracted
                 from GP-generated models (analytic functions) are also
                 exploited to determine the importance and relevance of
                 different haptic data types (e.g., force, position,
                 torque, and orientation) in user identity verification.
                 The results revealed that GP classifiers compare
                 favourably with the classical methods and use a much
                 fewer number of attributes (with simple function
                 sets).",
  notes =        "Also known as
                 \cite{Alsulaiman:2013:IVB:2457450.2457453} TOMCCAP",
}

Genetic Programming entries for Fawaz A Alsulaiman Nizar Sakr Julio J Valdes Abdulmotaleb El Saddik

Citations