Description

Fingerprint is widely used in identification and verification systems for the purpose of high degree of security. Usually, Gabor filter-based feature extraction for fingerprint recognition requires an additional step to detect the reference point in the fingerprint image and the features extracted by the Gabor filter are in very large dimensions. Traditionally, principal component analysis (PCA) and linear discriminator analysis (LDA/FLD) have been the standard approach for dimensionality reduction. FLD has proven to be more efficient than PCA in pattern recognition applications but it suffers from singularity or under-sampled problem. In this paper, we present a novel feature extraction method based on Gabor filter and Recursive Support Vector Machine (RSVM) to overcome this reference point detection overhead and singularity problem.