Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition
Biometrics 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 recognit...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | International Journal of Mathematics and Mathematical Sciences |
| Online Access: | http://dx.doi.org/10.1155/2021/5545488 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558426712965120 |
|---|---|
| author | Justice Kwame Appati Prince Kofi Nartey Ebenezer Owusu Ismail Wafaa Denwar |
| author_facet | Justice Kwame Appati Prince Kofi Nartey Ebenezer Owusu Ismail Wafaa Denwar |
| author_sort | Justice Kwame Appati |
| collection | DOAJ |
| description | Biometrics 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 methods. |
| format | Article |
| id | doaj-art-0008f361e26343099e0b22909f9c40aa |
| institution | Kabale University |
| issn | 0161-1712 1687-0425 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Mathematics and Mathematical Sciences |
| spelling | doaj-art-0008f361e26343099e0b22909f9c40aa2025-02-03T01:32:24ZengWileyInternational Journal of Mathematics and Mathematical Sciences0161-17121687-04252021-01-01202110.1155/2021/55454885545488Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint RecognitionJustice Kwame Appati0Prince Kofi Nartey1Ebenezer Owusu2Ismail Wafaa Denwar3Department of Computer Science, University of Ghana, Accra, GhanaDepartment of Computer Science, University of Ghana, Accra, GhanaDepartment of Computer Science, University of Ghana, Accra, GhanaDepartment of Computer Science, University of Ghana, Accra, GhanaBiometrics 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 methods.http://dx.doi.org/10.1155/2021/5545488 |
| spellingShingle | Justice Kwame Appati Prince Kofi Nartey Ebenezer Owusu Ismail Wafaa Denwar Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition International Journal of Mathematics and Mathematical Sciences |
| title | Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition |
| title_full | Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition |
| title_fullStr | Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition |
| title_full_unstemmed | Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition |
| title_short | Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition |
| title_sort | implementation of a transform minutiae fusion based model for fingerprint recognition |
| url | http://dx.doi.org/10.1155/2021/5545488 |
| work_keys_str_mv | AT justicekwameappati implementationofatransformminutiaefusionbasedmodelforfingerprintrecognition AT princekofinartey implementationofatransformminutiaefusionbasedmodelforfingerprintrecognition AT ebenezerowusu implementationofatransformminutiaefusionbasedmodelforfingerprintrecognition AT ismailwafaadenwar implementationofatransformminutiaefusionbasedmodelforfingerprintrecognition |