<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5291" mdate="2011-12-26 00:00:00"> <author>Deepti Tamrakar and Pritee Khanna</author> <title>Palmprint Recognition by Wavelet Transform with Competitive Index and PCA</title> <pages>1621 - 1625</pages> <year>2011</year> <volume>5</volume> <number>12</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/5291</ee> <url>https://publications.waset.org/vol/60</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>This manuscript presents, palmprint recognition by combining different texture extraction approaches with high accuracy. The Region of Interest (ROI) is decomposed into different frequencytime subbands by wavelet transform upto two levels and only the approximate image of two levels is selected, which is known as Approximate Image ROI (AIROI). This AIROI has information of principal lines of the palm. The Competitive Index is used as the features of the palmprint, in which six Gabor filters of different orientations convolve with the palmprint image to extract the orientation information from the image. The winnertakeall strategy is used to select dominant orientation for each pixel, which is known as Competitive Index. Further, PCA is applied to select highly uncorrelated Competitive Index features, to reduce the dimensions of the feature vector, and to project the features on Eigen space. The similarity of two palmprints is measured by the Euclidean distance metrics. The algorithm is tested on Hong Kong PolyU palmprint database. Different AIROI of different wavelet filter families are also tested with the Competitive Index and PCA. AIROI of db7 wavelet filter achievs Equal Error Rate (EER) of 0.0152 and Genuine Acceptance Rate (GAR) of 99.67 on the palm database of Hong Kong PolyU.</abstract> <index>Open Science Index 60, 2011</index> </article>