Genetic & Evolutionary Biometrics: Feature extraction from a Machine Learning perspective

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 GEFEML (Genetic and Evolutionary Feature Extraction - Machine Learning). GEFEML 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. GEFEML 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. GEFEML evolved FEs that used fewer patches, had comparable accuracy, and were 54% less expensive in terms of computational complexity.