Wavelet performance in biometric identification system according to users increase

Abstract

This work shows a simple and robust biometric identification system through the use of the palmprint. It proves the efficiency of the wavelet transform regardless of users' number. Firstly, the hand palm image with scale, rotation and translation invariance is isolated from the hand image recorded. Then, the "wavelet transform" is used to extract the texture features from gray-scale images. Three wavelet families, haar, daubechies and biortogonal are studied in order to get the best recognition rate. 1440 hand images of 144 people with 10 samples each one have been acquired by means of a commercial scanner with 150 dpi resolution. Support Vector Machine (SVM) is the main classifier used as identifier in closed mode. A recognition rate of 99.83% for 50 users and 99.76% for 144 users demonstrate the strong performance of wavelet transform in biometrics according to users increase.