Genetic and evolutionary methods for biometric feature reduction

  • Authors:
  • Aniesha Alford;Kelvin Bryant;Tamirat Abegaz;Gerry V. Dozier;John C. Kelly;Joseph Shelton;Lasanio Small;Jared Williams;Damon L. Woodard;Karl Ricanek

  • Affiliations:
  • Electrical Engineering, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computer Science, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computational Science and Engineering, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computer Science, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Electrical Engineering, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computer Science, North Carolina Agricultural and Technical State University, 1601 East Market St./ Greensboro, NC 27411, USA;Computer Science, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computer Science, North Carolina Agricultural and Technical State University, 1601 East Market St., Greensboro, NC 27411, USA;Computer Science, Clemson University, 314 McAdams Hall, Clemson, SC 29634-0974, USA;Computer Science, CIS Building, Room 2010, 601 South College Road, Wilmington, NC 28403, USA

  • Venue:
  • International Journal of Biometrics
  • Year:
  • 2012

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Abstract

In this paper, we investigate the use of Genetic and Evolutionary Computations (GECs) for feature selection (GEFeS), weighting (GEFeW), and hybrid weighting/selection (GEFeWS) in an attempt to increase recognition accuracy as well as reduce the number of features needed for biometric recognition. These GEC-based methods were first applied to a subset of 105 subjects taken from the Facial Recognition Grand Challenge (FRGC) dataset (FRGC-105) where several feature masks were evolved. The resulting feature masks were then tested on a larger subset taken from the FRGC dataset (FRGC-209) in an effort to investigate how well they generalise to unseen subjects. The results suggest that our GEC-based methods are effective in increasing the recognition accuracy and reducing the features needed for recognition. In addition, the evolved FRGC-105 feature masks generalised well on the unseen subjects within the FRGC-209 dataset.