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Local and Texture Features Based Palmprint Identification System Using KNN Classifier

Rungta International Journal of Electrical and Electronics Engineering

Volume 2 Issue 1 & 2

Published: 2017
Author(s) Name: Anish Kumar Vishwakarma | Author(s) Affiliation: Assist. Prof., Dept. of Elect. and Telecommn. Engg., Rungta College of Engg. and Tech., Chhattisgarh
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Palmprint is one of the very important biometric characteristics. In this, the identification process is divided into feature extraction, image acquisition, preprocessing, and matching with the available database. This paper includes the design of a biometric system for identification of human palm. The Proposed algorithm uses the local feature called SIFT and texture feature GLCM for extracting the features. The use of SIFT for feature extraction make the system robust. The palm images, which is used to extract feature is generally captured using a low-cost scanner. The algorithm is tested on IITD database which consist data of 235 users.

Keywords: Biometric, Feature extraction, Gray Level Cooccurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT).

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