Fingerprint image super resolution using sparse representation with ridge pattern prior by classification coupled dictionaries

Abstract

A new algorithm for reconstructing the fingerprint super-resolution (SR) image is presented. The basic idea of the algorithm is to reconstruct the SR image by using sparse representation with ridge pattern prior based on classification coupled dictionaries. First, the orientations of training patches are estimated by the weighted linear projection analysis. In the second procedure, the qualities of patches are assessed by the coherence of point orientations, the training patches are subsequently classified into eight groups based on their own orientations and qualities, and then the training patches of each class are selected from candidate patches by their own quality and the corresponding classification coupled dictionaries are learned. In the end, single SR fingerprint is reconstructed using sparse representation with ridge pattern by classification coupled dictionaries. The experiments with the database of FVC2000, FVC2004 and FVC 2006 are carried out using various SR reconstruction methods. The experiments show that the proposed method achieves better results in comparison with other methods and will help to improve the performance of automatic fingerprint identification system.

Feature extraction, recognition, and matching of damaged fingerprint: Application of deep learning network

“…(1) Image preprocessing: when fingerprint recognition device collects fingerprint image information, the image will have interference because of the noise and differences in human skin state or pressing force, so it needs preprocessing: the calculation formula of image enhancement 7 is as follows:…”

“…(1) Image preprocessing : when fingerprint recognition device collects fingerprint image information, the image will have interference because of the noise and differences in human skin state or pressing force, so it needs preprocessing: the calculation formula of image enhancement 7 is as follows: where ( i , j ) is the coordinates of an image pixel, G ( i , j ) is the gray value of the original image, G ′ ( i , j ) is the gray value of the enhanced image, and max( i , j ) and min( i , j ) are the upper and lower limits of gray levels of an image. Binarization is to convert an image into a black‐and‐white image.…”

Fingerprint enhancement using multi‐scale classification dictionaries with reduced dimensionality

“…It has made great achievements in face recognition [20,21], image denoising [22,23], image superresolution [24,25] and image fusion [26,27]. The relatively less active research on fingerprint image sparse representation also starts to attract the researcher's attention [28][29][30][31]. The focuses of these researches are mainly on exploring new and efficient methods to improve the performance of the AFIS, and some achievements and progresses have been made recently in this area.…”

Collaborative filtering model for enhancing fingerprint image

“…Fingerprint recognition is one of them. The performance of the fingerprint recognition largely relies on the quality of input fingerprint [5]. In practice, the fingerprint is often damaged for all kinds of reasons.…”