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Palm pattern recognition using scale invariant feature transform
Author(s) -
M. Kasiselvanathan,
V. Sangeetha,
A. Kalaiselvi
Publication year - 2020
Publication title -
international journal of intelligence and sustainable computing
Language(s) - English
Resource type - Journals
eISSN - 2517-7648
pISSN - 2517-763X
DOI - 10.1504/ijisc.2020.104826
Subject(s) - pattern recognition (psychology) , palm , artificial intelligence , invariant (physics) , scale (ratio) , scale invariance , palm print , scale invariant feature transform , computer science , mathematics , feature extraction , geography , biometrics , statistics , cartography , physics , quantum mechanics , mathematical physics
In this research paper, we propose an efficient scale invariant feature transform (SIFT) for palm pattern recognition. A fingerprint recognition which is efficient for individual authentication based on fingerprint pattern. This method leads to fraudulent because it could be extracted easily from individuals. The SIFT method based on feature detection overcomes the above problem and is a combination of fast key point detector and visual descriptor. Using SIFT method contactless palm pattern images can be acquired, matched, recognised, authenticated and their matching performance are simulated using OpenCV. The experimental results show that SIFT method provides significantly fast and improved performance than the conventional methods like oriented FAST and rotated BRIEF (ORB).

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