Title: 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

Addresses: 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

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.

Keywords: genetic computation; evolutionary computation; biometrics; feature selection; feature weighting; feature reduction; recognition accuracy; facial recognition; feature recognition.

DOI: 10.1504/IJBM.2012.047642

International Journal of Biometrics, 2012 Vol.4 No.3, pp.220 - 245

Received: 13 May 2011
Accepted: 12 Jul 2011

Published online: 06 Jul 2012 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article