A comparison of GEC-based feature selection and weighting for multimodal biometric recognition

Aniesha Alford,Khary Popplewell,Gerry V. Dozier,Kelvin S. Bryant,John Kelly,Joshua Adams,Tamirat Abegaz,Joseph Shelton,Karl Ricanek,Damon L. Woodard

IEEE Congress on Evolutionary Computation（2011）

Cited 4|Views6

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Abstract

In this paper, we compare the performance of a Steady-State Genetic Algorithm (SSGA) and an Estimation of Distribution Algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeWSSGA) produces higher average recognition accuracies, while EDA-based feature selection (GEFeSEDA) performs better at reducing the number of features needed for recognition.

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Key words

feature weighting,distributed algorithms,eda,estimation of distribution algorithm,eigenface,feature selection,steady-state genetic algorithm,multimodal biometric recognition,local binary pattern,biometrics (access control),feature extraction,genetic algorithms,gec,ssga,face,face recognition,accuracy,iris recognition

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