Abstract
Genetic & Evolutionary Biometrics (GEB) is a newly emerging area of study devoted to the design, analysis, and application of genetic and evolutionary computing to the field of biometrics. In this paper, we present a GEB application called GEFE ML (Genetic and Evolutionary Feature Extraction - Machine Learning). GEFE ML incorporates a machine learning technique, referred to as cross validation, in an effort to evolve a population of local binary pattern feature extractors (FEs) that generalize well to unseen subjects. GEFE ML was trained on a dataset taken from the FRGC database and generalized well on two test sets of unseen subjects taken from the FRGC and MORPH databases. GEFE ML evolved FEs that used fewer patches, had comparable accuracy, and were 54% less expensive in terms of computational complexity.
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