Genetic & Evolutionary Biometrics: Hybrid feature selection and weighting for a multi-modal biometric system

The Genetic & Evolutionary Computation (GEC) research community is seeing the emergence of a new and exciting subarea, referred to as Genetic & Evolutionary Biometrics (GEB), as GECs are increasingly being applied to a variety of biometric problems. In this paper, we present successful GEB techniques for multi-biometric fusion and multi-biometric feature selection and weighting. The first technique, known as GEF (Genetic & Evolutionary Fusion), seeks to optimize weights for score-level fusion. The second technique is known as GEFeWS_ML (Genetic & Evolutionary Feature Weighting and Selection-Machine Learning). The goal of GEFeWS_ML is to evolve feature masks (FMs) that achieve high recognition accuracy, use a low percentage of features, and generalize well to unseen subjects. GEFeWS_ML differs from the other GEB techniques for feature selection and weighting in that it incorporates cross validation in an effort to evolve FMs that generalize well to unseen subjects.