Abstract

This paper presents an effective method for decision level fusion of fingerprint and iris biometrics using binary ant colony optimization (ACO) technique to identify the imposter instances. ACO is an evolutionary method. The selection of a proper set of optimization parameters for ACO is a multi-objective decision making optimization problem. Initially the matching scores for individual biometric classifiers are computed. Next, a ACO-based procedure is followed to simultaneously optimize the parameters and the fusion rules for fingerprint and iris biometrics. The proposed method has been found to perform satisfactorily on several benchmark datasets.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.