Analyzing the cross-generalization ability of a hybrid Genetic& Evolutionary application for multibiometric feature Weighting and Selection

Genetic & Evolutionary Biometrics (GEB) is a new field of study devoted to the use of Genetic&Evolutionary Computations to solve some of the traditional problems within the field of biometrics. In this paper, we evaluate the performances of two GEB applications, Genetic&Evolutionary Fusion (GEF) and Genetic&Evolutionary Feature Weighting/Selection-Machine Learning (GEFeWSML), on the FRGC and MORPH databases. We then investigate the ability of the evolved weights and feature masks (FMs) to generalize across datasets. Our results showed that the GEB applications were robust, achieving high recognition accuracies across the datasets. In addition, the FMs achieved these recognition accuracies while using less than 50% of the originally extracted features. Copyright is held by the author/owner(s).