Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification
Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convol...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2019-04-01
|
| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2018-0289 |
| _version_ | 1818664927896797184 |
|---|---|
| author | Praseetha V.M. Bayezeed Saad Vadivel S. |
| author_facet | Praseetha V.M. Bayezeed Saad Vadivel S. |
| author_sort | Praseetha V.M. |
| collection | DOAJ |
| description | Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models. |
| first_indexed | 2024-12-17T05:40:31Z |
| format | Article |
| id | doaj.art-eb6066fe3f894fc0a989bdec777408dd |
| institution | Directory Open Access Journal |
| issn | 0334-1860 2191-026X |
| language | English |
| last_indexed | 2024-12-17T05:40:31Z |
| publishDate | 2019-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-eb6066fe3f894fc0a989bdec777408dd2022-12-21T22:01:28ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2019-04-012911379138710.1515/jisys-2018-0289Secure Fingerprint Authentication Using Deep Learning and Minutiae VerificationPraseetha V.M.0Bayezeed Saad1Vadivel S.2Birla Institute of Technology and Science Pilani, P.O. Box 345055, Dubai, United Arab EmiratesBirla Institute of Technology and Science Pilani, Dubai, United Arab EmiratesBirla Institute of Technology and Science Pilani, Dubai, United Arab EmiratesNowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models.https://doi.org/10.1515/jisys-2018-0289biometricsdeep learningconvolutional neural networkinception modelminutiaefingerprint68t10 |
| spellingShingle | Praseetha V.M. Bayezeed Saad Vadivel S. Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification Journal of Intelligent Systems biometrics deep learning convolutional neural network inception model minutiae fingerprint 68t10 |
| title | Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification |
| title_full | Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification |
| title_fullStr | Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification |
| title_full_unstemmed | Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification |
| title_short | Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification |
| title_sort | secure fingerprint authentication using deep learning and minutiae verification |
| topic | biometrics deep learning convolutional neural network inception model minutiae fingerprint 68t10 |
| url | https://doi.org/10.1515/jisys-2018-0289 |
| work_keys_str_mv | AT praseethavm securefingerprintauthenticationusingdeeplearningandminutiaeverification AT bayezeedsaad securefingerprintauthenticationusingdeeplearningandminutiaeverification AT vadivels securefingerprintauthenticationusingdeeplearningandminutiaeverification |