PALMPRINT CLASSIFICATION USING FIXED SIFT DESCRIPTORS NUMBER

Authors

  • ANCA IGNAT University “Alexandru Ioan Cuza” of Iaşi, Romania, Faculty of Computer Science, str. Berthelot 16, 700483
  • IOAN PĂVĂLOI Institute of Computer Science, Romanian Academy, Iași Branch, str. T. Codrescu, nr. 2, 700481, Iaşi, Romania

Keywords:

Scale-invariant feature transform keypoints, Keypoint matching, Palmprint classification

Abstract

In this article we use, for palmprint feature extraction, descriptors generated with SIFT (Scale-invariant feature transform) algorithm. The main idea was to generate for each image in the dataset, the same number of keypoints. We deduced an algorithm that, for a given image, computes a fixed number of SIFT keypoints. The matching procedure is based on the nearest neighbor ratio equation. To test the efficacy of our method, we performed experiments on five well-known palmprint databases. The experimental results indicate that this type of approach yields very good classification results. Our results are better than those obtained in some recent papers.

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Published

01.07.2022

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Section

Génie biomédical / Biomedical Engineering