Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Abstract

In this paper, we present a new approach for fingerprint classification based on Discrete Fourier Transform (DFT) and nonlinear discriminant analysis. Utilizing the Discrete Fourier Transform and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.

On the Introduction of Secondary Fingerprint Classification

“…Some practitioners usually refer to the circular pattern as a whorl (Park & Park, 2005), while the two-loop pattern is referred to as a twin loop (Karu & Jain, 1996). The similarity, however, between the two patterns is that they both have cores located next to each other in the central area of the fingerprint, which is the main reason why Msiza et al (2009) grouped these two patterns into the same class, called the Central Twins class.…”

An Agent-Based Paradigm for Free-Hand Sketch Recognition

“…As traditional techniques for classification, we can find the fuzzy logic, distances from ideal shape, linear and non-linear discriminant analysis, etc. ( [14][15][16][17]). These traditional techniques have the drawback of being rigid and the more symbols are contained in the catalog the worse results are obtained.…”

High-resolution orientation field estimation based on multi-scale Gaussian filter

“…It depicts the fingerprint ridge's directional information and is displayed as a vector tangent to the ridge. It is used extensively in fingerprint recognition, in particular, for fingerprint image enhancement [1], fingerprint classification [2,3], fingerprint registration, and singularity detection [4]. Several approaches have been developed for OF estimation and can be broadly classified into three main categories: 1) Filter bank-based, 2) Modelbased, and 3) Gradient-based.…”