Implementation of a Transform-Minutiae Fusion-BasedModel for Fingerprint Recognition

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dc.contributor.authorAppati, J.K.
dc.contributor.authorNartey, P.K.
dc.contributor.authorOwusu, E.
dc.contributor.authorDenwar, I.W.
dc.date.accessioned2022-01-14T15:42:42Z
dc.date.available2022-01-14T15:42:42Z
dc.date.issued2021
dc.descriptionResearch Articleen_US
dc.description.abstractBiometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methodsen_US
dc.identifier.otherhttps://doi.org/10.1155/2021/5545488
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/37643
dc.language.isoenen_US
dc.publisherHindawien_US
dc.titleImplementation of a Transform-Minutiae Fusion-BasedModel for Fingerprint Recognitionen_US
dc.typeArticleen_US

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