A comparison of GEC-based feature selection and weighting for multimodal biometric recognition

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 (GEFeW
_SSGA
) produces higher average recognition accuracies, while EDA-based feature selection (GEFeS
_EDA
) performs better at reducing the number of features needed for recognition.