Transform based approaches for Palmprint Identification

Abstract

In this paper, identification of a person is carried out using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients as palmprint features. Dimensionality reduction was carried out using Principal Component Analysis (PCA) and the reduced features were classified using Support Vector Machine (SVM). The segmented palmprint images used in this study are from the IITD palmprint database. Here, we describe the palmprint identification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Experiments were developed on a database of 100 images from 20 individuals. This study shows that DCT is better for palmprint identification with an accuracy of 99% compared to DWT which gives 96.66% accuracy.