scholarly journals A Fingerprint Recognition Algorithm Combining Phase-Based Image Matching and Feature-Based Matching

2005 ◽  
pp. 316-325 ◽  
Author(s):  
Koichi Ito ◽  
Ayumi Morita ◽  
Takafumi Aoki ◽  
Hiroshi Nakajima ◽  
Koji Kobayashi ◽  
...  
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Feature Based

An improved fingerprint image matching and multi-view fingerprint recognition algorithm

2018 ◽  
Vol 35 (3-4) ◽  
pp. 341-354
Author(s):  
Dewen SENG ◽  
Hanggi ZHANG ◽  
Xujian FANG ◽  
Xuefeng ZHANG ◽  
Jing CHEN
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

scholarly journals A new median filter based fingerprint recognition algorithm

2011 ◽  
Vol 3 ◽  
pp. 859-865 ◽  
Author(s):  
Ayyüce M. Kızrak ◽  
Figen Özen
Keyword(s):  
Median Filter ◽  
Recognition Algorithm ◽  
Fingerprint Recognition

A Rock Cracks Dynamic Recognition Algorithm Based on Fingerprint

2012 ◽  
Vol 466-467 ◽  
pp. 176-180
Author(s):  
Ling Ling Xiao ◽  
Yan Ting Liu
Keyword(s):  
Labor Force ◽  
Rock Fracture ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Texture Recognition ◽  
Detection Technology ◽  
Dynamic Recognition ◽  
Rock Texture

Traditional detection technology of rock cracks in mine is asking somebody to go deep into the mine and detect them. This method doesn’t only waste so much labor force, but also lose timeliness. It will bring amount of risk to the workers and the results will contain subjectivity. As complex texture of rock is similar to the fingerprint, we give a rock fracture recognition algorithm based on fingerprint. In this paper, the fingerprint recognition algorithm is introduced into the rock texture recognition, and to be improved by us. We demonstrate the algorithm is feasibility with experiments.

Multi-Template Processing of Fingerprint Recognition Algorithm

2014 ◽  
Vol 610 ◽  
pp. 332-338
Author(s):  
Lian Ying Zou ◽  
Ying Zhou ◽  
Xiang Dong ◽  
Yu Chen
Keyword(s):  
Image Processing ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Ridge Line ◽  
Black And White ◽  
White Point ◽  
Direction Filter ◽  
Fingerprint Feature ◽  
Line Texture

Using multi-template processing algorithm, the fingerprint features are accurately collected. Through normalization, make the black and white point contrast of the fingerprint image more obviously, strengthen the ridge line texture. Direction calculating algorithm is based on the grey value of the neighborhood pixels. It can be implemented simply and speedily. Through direction filter, noises can be removed, and the contrast of the fingerprint’s ridge lines and valley lines can be enhanced. After binary converting, all information of the fingerprint is stored with 0 and 1. The effect of thinning is to make the fingerprint image more distinct to extract the fingerprint feature point easily. These steps had been implemented on Altera DE2 board with HDL codes. The experimental results indicate that the multi-template algorithm of fingerprint image processing is correct and practicable.

Sign in / Sign up

Export Citation Format

Share Document