Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/12/6122 |
| _version_ | 1827662590750228480 |
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| author | Samy Bakheet Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis |
| author_facet | Samy Bakheet Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis |
| author_sort | Samy Bakheet |
| collection | DOAJ |
| description | Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%. |
| first_indexed | 2024-03-10T00:30:05Z |
| format | Article |
| id | doaj.art-ef2959f83aa846399d4f0151eaf281b5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:30:05Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ef2959f83aa846399d4f0151eaf281b52023-11-23T15:28:08ZengMDPI AGApplied Sciences2076-34172022-06-011212612210.3390/app12126122Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT FeaturesSamy Bakheet0Shtwai Alsubai1Abdullah Alqahtani2Adel Binbusayyis3Faculty of Computers and Artificial Intelligence, Sohag University, Sohag 82524, EgyptCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi ArabiaMinutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%.https://www.mdpi.com/2076-3417/12/12/6122fingerprint minutiaeSIFT feature detectionfeature matchingFVC2004 databaseEER |
| spellingShingle | Samy Bakheet Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features Applied Sciences fingerprint minutiae SIFT feature detection feature matching FVC2004 database EER |
| title | Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features |
| title_full | Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features |
| title_fullStr | Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features |
| title_full_unstemmed | Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features |
| title_short | Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features |
| title_sort | robust fingerprint minutiae extraction and matching based on improved sift features |
| topic | fingerprint minutiae SIFT feature detection feature matching FVC2004 database EER |
| url | https://www.mdpi.com/2076-3417/12/12/6122 |
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