A comparison of GEC-based feature selection and weighting for multimodal biometric recognition
Aniesha Alford, Khary Popplewell, Gerry Vernon Dozier, Kelvin S. Bryant, John Charles Kelly, Josh Adams, Tamirat T. Abegaz, Joseph Shelton, Karl Ricanek, Damon L. Woodard · 2011
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.