Research Paper
Year: 2021 | Month: September | Volume: 8 | Issue: 9 | Pages: 518-526
DOI: https://doi.org/10.52403/ijrr.20210965
Feature Level Fusion of Biometric Images Using Modified Clonal Selection Algorithm
Adedeji, Oluyinka Titilayo1, Amusan, Elizabeth Adedoyin2, Alade, Oluwaseun. Modupe3, Fenwa, Olusayo Deborah4
1Senior Lecturer, Department of Information System, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
2,3Senior Lecturer, Department of Cyber Security Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo State Nigeria.
4Associate Professor, Department of Cyber Security Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo State Nigeria.
Corresponding Authors: Amusan E.A, Alade O.M. and Fenwa, O. D.
ABSTRACT
In feature level fusion, biometric features must be combined such that each trait is combined so as to maintain feature-balance. To achieve this, Modified Clonal Selection Algorithm was employed for feature level fusion of Face, Iris and Fingerprints. Modified Clonal Selection Algorithm (MCSA) which is characterized by feature-balance maintenance capability and low computational complexity was developed and implemented for feature level fusion. The standard Tournament Selection Method (TSM) was modified by performing tournaments among neighbours rather than by random selection to reduce the between-group selection pressure associated with the standard TSM. Clonal Selection algorithm was formulated by incorporating the Modified Tournament Selection Method (MTSM) into its selection phase. Quantitative experimental results showed that the systems fused with MCSA has a higher recognition accuracy than those fused with CSA, also with a lower recognition time.
Keywords: Biometrics, Feature level Fusion, Multibiometrics, Modified Clonal Selection Algorithm, Recognition Accuracy, Recognition Time.
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