English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, Harris Corner Detector is proposed as a corner detection technique to extract palmprint features in the form of corners. Here, hamming distance similarity measurement using sliding window method is used as a feature matching method for the corners detected. The aim of using hamming distance method for corner matching is the non-dependency of the method with the number of corners detected. So, the comparison (matching) time will be constant with hamming distance feature matching method. We used the same feature matching technique in edge detection and got good results. In this paper, palmprint features are analyzed on different sigma, threshold and radius values. Experiments were developed on a database of 600 images from 100 individuals, with five image samples per individual for training and one image sample per individual for testing. The experimental results indicate that using Harris corner detector and Hamming distance using sliding window, recognition rate of 97.5% can be achieved.

Harris Operator Corner Detection using Sliding Window Method