Feature-based technique for partial fingerprint matching
This paper deals with filter bank to match partial fingerprints. The method uses both local and global details in a fingerprint and defined as fixed length feature vector. Final matching is done by calculating Euclidean distance between the two corresponding feature vectors. Input fingerprints are m...
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2011
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my.upm.eprints.477612016-07-15T03:42:51Z http://psasir.upm.edu.my/id/eprint/47761/ Feature-based technique for partial fingerprint matching Vijayaprasad, Perumal Sulaiman, Md. Nasir Mustapha, Norwati O. K. Rahmat, Rahmita Wirza This paper deals with filter bank to match partial fingerprints. The method uses both local and global details in a fingerprint and defined as fixed length feature vector. Final matching is done by calculating Euclidean distance between the two corresponding feature vectors. Input fingerprints are made to 256 × 256 image of 8-bit grayscale with 500 dots per inch. Finger code is calculated by rotating the input images. Normalization is applied after cropping and sectoring the fingerprint image and finally gabor filters are used with same angle of rotation. Results proved that our method is better in false acceptance and total error rate when compared to the minutiae based approach. FVC2004 database is used for testing and comparison. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47761/1/Feature-based%20technique%20for%20partial%20fingerprint%20matching.pdf Vijayaprasad, Perumal and Sulaiman, Md. Nasir and Mustapha, Norwati and O. K. Rahmat, Rahmita Wirza (2011) Feature-based technique for partial fingerprint matching. In: 7th International Conference on Information Technology in Asia (CITA 2011), 12-13 July 2011, Kuching, Sarawak. . 10.1109/CITA.2011.5999524 |
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This paper deals with filter bank to match partial fingerprints. The method uses both local and global details in a fingerprint and defined as fixed length feature vector. Final matching is done by calculating Euclidean distance between the two corresponding feature vectors. Input fingerprints are made to 256 × 256 image of 8-bit grayscale with 500 dots per inch. Finger code is calculated by rotating the input images. Normalization is applied after cropping and sectoring the fingerprint image and finally gabor filters are used with same angle of rotation. Results proved that our method is better in false acceptance and total error rate when compared to the minutiae based approach. FVC2004 database is used for testing and comparison. |
format |
Conference or Workshop Item |
author |
Vijayaprasad, Perumal Sulaiman, Md. Nasir Mustapha, Norwati O. K. Rahmat, Rahmita Wirza |
spellingShingle |
Vijayaprasad, Perumal Sulaiman, Md. Nasir Mustapha, Norwati O. K. Rahmat, Rahmita Wirza Feature-based technique for partial fingerprint matching |
author_facet |
Vijayaprasad, Perumal Sulaiman, Md. Nasir Mustapha, Norwati O. K. Rahmat, Rahmita Wirza |
author_sort |
Vijayaprasad, Perumal |
title |
Feature-based technique for partial fingerprint matching |
title_short |
Feature-based technique for partial fingerprint matching |
title_full |
Feature-based technique for partial fingerprint matching |
title_fullStr |
Feature-based technique for partial fingerprint matching |
title_full_unstemmed |
Feature-based technique for partial fingerprint matching |
title_sort |
feature-based technique for partial fingerprint matching |
publisher |
IEEE |
publishDate |
2011 |
url |
http://psasir.upm.edu.my/id/eprint/47761/1/Feature-based%20technique%20for%20partial%20fingerprint%20matching.pdf http://psasir.upm.edu.my/id/eprint/47761/ |
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