Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA)
Despite recent advances in the area of fingerprint identification, fingerprint enrolment continues to be a challenging pattern recognition problem. The first step to this problem is the enhancement of landmarks as well as precise minutiae points (ridge bifurcation and ridge ending), core, plain ridg...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/289/ http://shdl.mmu.edu.my/289/1/Proceedings_copy_Precise_fingerprint_enrolment_through_PCA.pdf |
| _version_ | 1848789465456181248 |
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| author | Islam, Md. Rajibul Sayeed, Md. Shohel Samraj, Andrews |
| author_facet | Islam, Md. Rajibul Sayeed, Md. Shohel Samraj, Andrews |
| author_sort | Islam, Md. Rajibul |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Despite recent advances in the area of fingerprint identification, fingerprint enrolment continues to be a challenging pattern recognition problem. The first step to this problem is the enhancement of landmarks as well as precise minutiae points (ridge bifurcation and ridge ending), core, plain ridges from a print. Once enhanced, these fingerprint images are then ready to extract features and store into a database. Later these are compared to all sets on file in search of a match. The accurate fingerprint image is the basis for the entire identification and matching process. Various enhancement approaches have been proposed in the literature, each with its own merits and degree of success. The most common approach is to enhance and store the precise fingerprint image through normalization, orientation, frequencies calculation, contextual filtering and then binarisation and masking. Our emphasis in this paper is to enhance and store the fingerprint image accurately using Projection Incorporated Subspace based on Principal Component Analysis (PCA). In particular, we have implemented the methods based on eigenspace representations and neural network classifiers. Moreover, we present preliminary results of an attempt to mingle the outputs of these methods using a clustering algorithm unique to this type of problem. |
| first_indexed | 2025-11-14T17:57:09Z |
| format | Conference or Workshop Item |
| id | mmu-289 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:57:09Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-2892010-05-04T07:32:41Z http://shdl.mmu.edu.my/289/ Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) Islam, Md. Rajibul Sayeed, Md. Shohel Samraj, Andrews QA75.5-76.95 Electronic computers. Computer science QA76.75-76.765 Computer software Despite recent advances in the area of fingerprint identification, fingerprint enrolment continues to be a challenging pattern recognition problem. The first step to this problem is the enhancement of landmarks as well as precise minutiae points (ridge bifurcation and ridge ending), core, plain ridges from a print. Once enhanced, these fingerprint images are then ready to extract features and store into a database. Later these are compared to all sets on file in search of a match. The accurate fingerprint image is the basis for the entire identification and matching process. Various enhancement approaches have been proposed in the literature, each with its own merits and degree of success. The most common approach is to enhance and store the precise fingerprint image through normalization, orientation, frequencies calculation, contextual filtering and then binarisation and masking. Our emphasis in this paper is to enhance and store the fingerprint image accurately using Projection Incorporated Subspace based on Principal Component Analysis (PCA). In particular, we have implemented the methods based on eigenspace representations and neural network classifiers. Moreover, we present preliminary results of an attempt to mingle the outputs of these methods using a clustering algorithm unique to this type of problem. 2007-11-28 Conference or Workshop Item PeerReviewed application/pdf en http://shdl.mmu.edu.my/289/1/Proceedings_copy_Precise_fingerprint_enrolment_through_PCA.pdf Islam, Md. Rajibul and Sayeed, Md. Shohel and Samraj, Andrews (2007) Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA). In: 2nd International Conference on Informatics (Informatics 2007), 27-28 Nov 2007, Kuala Lumpur, Malaysia. http://informatics.fsktm.um.edu.my/ |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science QA76.75-76.765 Computer software Islam, Md. Rajibul Sayeed, Md. Shohel Samraj, Andrews Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title | Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title_full | Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title_fullStr | Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title_full_unstemmed | Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title_short | Precise Fingerprint Enrolment through Projection Incorporated Subspace Based on Principal Component Analysis (PCA) |
| title_sort | precise fingerprint enrolment through projection incorporated subspace based on principal component analysis (pca) |
| topic | QA75.5-76.95 Electronic computers. Computer science QA76.75-76.765 Computer software |
| url | http://shdl.mmu.edu.my/289/ http://shdl.mmu.edu.my/289/ http://shdl.mmu.edu.my/289/1/Proceedings_copy_Precise_fingerprint_enrolment_through_PCA.pdf |