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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pradeep, S.N. Jain, M.D. Prakash, C. Balasubramanian, R. |
| Copyright Year | 2006 |
| Description | Author affiliation: Sarnoff Innovative Technol., Bangalore (Pradeep, S.N.) |
| Abstract | We introduce palmprint recognition, one of the most reliable personal identification methods in the biometric technology. In this paper, a new approach to the palmprint matching by constructing local and global line feature structures is presented. The datum point of palmprint acts as an important registration due to its remarkable advantage about its spatial location. Initially we define all possible local line feature structures constructed with adjacent lines, around datum point. Through a first level match using these local line feature structures, we get best matched line features. Using these best matched line features, we construct a global line feature structure of palmprint with datum point as reference. This global line feature structure is spread across in four quadrants, datum point being the origin. We then carry out a second level match using this global structure of palmprint to reliably determine its uniqueness. The two levels of matching using local and global line feature structures helps in effective palmprint recognition. With several palmprint images, we tested out proposed verification system and the experimental result shows that the performance of our algorithm is good. |
| Starting Page | 664 |
| Ending Page | 669 |
| File Size | 243464 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780394909 |
| DOI | 10.1109/IJCNN.2006.246747 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-16 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biometrics Geometry System testing Humans Stability Authentication Image resolution Fingerprint recognition Robustness Feature extraction |
| Content Type | Text |
| Resource Type | Article |
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