Palmprint recognition based on subspace analysis of gabor filter bank

Abstract

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) of Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these feature are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area one should carefully consider this fact when selecting the appropriate palm region for feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances evidence by an Equal Error Rate (EER) of 0.03%. © 2009 Springer-Verlag Berlin Heidelberg.