Fingerprint Image Enhancement with Second Derivative Gaussian Filter and Directional Wavelet Transform

Abstract

In this paper, we propose a technique for enhancing the quality of fingerprint images. Directional wavelet transform and second derivative of a Gaussian filter are applied. The original fingerprint image is decomposed into approximation and detail sub-images. To each sub-dimension a directional filter: second derivative of Gaussian filter is applied for tuning up the image features. The enhanced image is measured for its improvement by testing the success of core point identification where Poincare technique is used. The commonly-well-known database FVC-2004 is used in this study. The obtained results offer clean visualization as well as the increase the success of true core point detection.

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images

“…The wavelet transform has been widely used for curvilinear structure enhancement in biomedical images. In [34], the authors propose a new approach to enhance the curvilinear structures in fingerprint images by involving the second derivative of a Gaussian filter with a directional wavelet transform. Another approach combines the Discrete Wavelet Transform and morphological filter (opening and closing) to enhance curvilinear structures in MRI images [35].…”

Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

“…The wavelet transform has been widely used for curvilinear structure enhancement in biomedical images. In [21], the authors propose a new approach to enhance the curvilinear structures in fingerprint images by involving the second derivative of a Gaussian filter with a directional wavelet transform. Another approach combines the Discrete Wavelet Transform and morphological filter (opening and closing) to enhance curvilinear structures in MRI images [22].…”

A New Open Curve Detection Algorithm for Extracting the Laser Lines on the Road

“…They still suffer from the problems of determining the correct global or local thresholds in processing the intensity inhomogeneous images automatically, and these methods always need to link the detected discontinuous segments and falsely extraction results always occur due to the noise influence. In this paper, we propose a new open curve detection algorithm via the line enhance filtering [6,7] and the curve evolution method [8] based on the imaging characteristics of the laser line projected onto the road surface. The laser line in the image is enhanced by a second order of Gaussian filtering.…”

“…where ( ) y x φ = is the initial curve. The finite difference method is used to discretize the partial differential equation in (7) for the numerical solution of ϕ(x, t). Let h be the space step, ∆t be the time step, x i = ih be the grid points for 1≤i≤w, and…”