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 ) pro...

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Bibliographic Details
Main Authors: Alford, Aniesha, Popplewell, Khary, Dozier, Gerry, Bryant, Kelvin, Kelly, John, Adams, Josh, Abegaz, Tamirat, Shelton, Joseph, Ricanek, Karl, Woodard, Damon L.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Eigenface
Estimation of Distribution Algorithm
Face
Face recognition
Feature extraction
Feature Selection
Feature Weighting
Iris recognition
Local Binary Pattern
Probes
Steady-State Genetic Algorithm
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Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2011.5949959