Title :
SSGA & EDA based feature selection and weighting for face recognition
Author :
Abegaz, Tamirat ; Dozier, Gerry ; Bryant, Kelvin ; Adams, Joshua ; Shelton, Joseph ; Ricanek, Karl ; Woodard, Damon L.
Author_Institution :
Center for Adv. Studies in Identity Sci., North Carolina A&T State Univ., NC, USA
Abstract :
In this paper, we compare genetic and evolutionary feature selection (GEFeS) and weighting (GEFeW) using a number of biometric datasets. GEFeS and GEFeW have been implemented as instances of Steady-State Genetic and Estimation of Distribution Algorithms. Our results show that GEFeS and GEFeW dramatically improve recognition accuracy as well as reduce the number of features needed for facial recognition. Our results also show that the Estimation of Distribution Algorithm implementation of GEFeW has the best overall performance.
Keywords :
biometrics (access control); face recognition; feature extraction; genetic algorithms; EDA; SSGA; biometric dataset; distribution algorithm estimation; face recognition accuracy; facial recognition; genetic feature selection; steady-state genetic algorithm; Accuracy; Eigenvalues and eigenfunctions; Estimation; Face recognition; Feature extraction; Histograms; Principal component analysis; Eigenface; Estimation of Distribution Algorithm; Face Recognition; Feature Selection; Steady State Genetic Algorithm;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949776
