Study of Fingerprint Enhancement and Matching

Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy

A DoG based Approach for Fingerprint Image Enhancement

Fingerprints have been the most accepted tool for personal identification since many decades. It is also an invaluable tool for law enforcement and forensics for over a century, motivating the research in Automated fingerprint-based identification, an application of biometric system. The matching or identification accuracy using fingerprints has been shown to be very high. The theory on the uniqueness of fingerprint minutiae leads to the steps in studying the statistics of extracting the minutiae features reliably. Fingerprint images obtained through various sources are rarely of perfect quality. They may be degraded or noisy due to variations in skin or poor scanning technique or due to poor impression condition. Hence enhancement techniques are applied on fingerprint images prior to the minutiae point extraction to get sure of less spurious and more accurate minutiae points from the reliable minutiae location. This thesis focuses on fingerprint image enhancement techniques through histogram equalization applied locally on the degraded image. The proposed work is based on the Laplacian pyramid framework that decomposes the input image into a number of band-pass images to improve the local contrast, as well as the local edge information. The resultant image is passed through the regular methodologies of fingerprint, like ridge orientation, ridge frequency calculation, filtering, binarization and finally the morphological operation thinning. Experiments using different texture of images are conducted to enhance the images and to show a comparative result in terms of number of minutiae extracted from them along with the spurious and actual number existing in each enhanced image. Experimental results out performs well to overcome the counterpart of enhancement technique

Alternative Framework for Enhancing Image Quality of the Fingerprint

Fingerprint Identification is one of the most popular biometric methods used to verify and identify a person; formally it is defined as “The pattern of ridges and furrows on an individual finger”. Ridges are the lines in thumb and furrow is shallow trench of skin on an individual’s finger. Furrow is also referred to as valley. The combination of ridges and furrows makes an individual’s fingerprint and it’s called minutiae. A critical step in Fingerprint is to automatically and reliably extract minutiae from input finger print images. However the performance of the Minutiae extraction algorithm and other fingerprint recognition techniques relies heavily on the quality of the input fingerprint image. In order to ensure that the performance of the minutiae extraction algorithm will be robust with respect to the quality of input fingerprint images, an enhancement framework which can improve the clarity of the ridge structures is necessary. In this paper we reviewed, analyzed and evaluated some of the existing frameworks for image enhancement of the fingerprint which used Image or Minutiae Based and anticipated outcome of this effort is alternative hybrid framework of enhancing fingerprint image quality, that is a more proactive and consistent approach of improvement, 100 fingerprint from 4 DBs of FVC2006 was used for experiment and tested in MATLAB & Adobe Photoshop image processing Software’s, result indicates that proposed enhancement framework improved gap knowledge of the analyzed existing frameworks and Fingerprint were enhanced by correcting and removing noise from the input fingerprint

Minutiae point exraction for skeleton-based fingerprint image

A reliable personal identification is necessary due to the growing importance of information technology and the necessity of protection and access restriction. The key task is to identify the owner of the security system. A biometric security system verifies user identity by comparing the behavioral or physiological trait of the owner. Fingerprints are the oldest and most widely used form of biometric identification because of their high acceptability, immutability and individuality. Local characteristic called minutiae points represent fingerprints where the most prominent points are end point and bifurcation point. This project is a software design of minutiae point extraction for skeleton-based fingerprint image. There are four main components in the software design which are image pre-processing, minutiae extraction, post-processing and the graphical user interface (GUI). In the image pre-processing, image enhancement were executed which include loading an input image, binarize the image and thinning the image. In the minutiae extraction stage, end points and bifurcation points were located. The post-processing stage execute the removal of the false minutiae out of the fingerprint image and lastly, the graphical user interface (GUI) will access the image repository and display the output image at each processing stage hence, as the interface between the user and the software design

Microelectronics implementation of directional image-based fuzzy templates for fingerprints

Fingerprint orientation image, also called directional image, is a widely used method in fingerprint recognition. It helps in classification (accelerating fingerprint identification process) as well as in preprocessing or processing steps (such as fingerprint enhancement or minutiae extraction). Hence, efficient storage of directional image-based information is relevant to achieve low-cost templates not only for “match on card” but also for “authentication on card” solutions. This paper describes how to obtain a fuzzy model to describe the directional image of a fingerprint and how this model can be implemented in hardware efficiently. The CAD tools of the Xfuzzy 3 environment have been employed to accelerate the fuzzy modeling process as well as to implement the directional image-based template into both an FPGA from Xilinx and an ASIC

A new three-stage scheme for fingerprint enhancement and its impact on fingerprint recognition

In order to provide safety and security from fraudulent acts, it is necessary to use a reliable biometric identifier. Fingerprint is considered to be one of most effective biometric identifiers because of its universal characteristics. The recognition rate of identification/verification systems depends to a great extent on the quality of the fingerprint image. In a fingerprint recognition system, there are two main phases: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching using those extracted features to find the correspondence and similarity between the fingerprint images. The low quality of fingerprint images provides false minutiae at the stage of feature extraction and reduces the recognition rate of minutiae-based fingerprint matching systems. Use of enhanced fingerprint images improves the recognition rate but at the expense of a substantially increased complexity. The objective of this research is to develop an efficient and cost-effective scheme for enhancing fingerprint images that can improve minutiae extraction rate as well as effectively improve the recognition rate of a minutiae-based fingerprint matching system. In the first part of this thesis, a novel low-complexity three-stage scheme for the enhancement of fingerprint images is developed. In the first stage of the scheme, a linear diffusion filter driven by an orientation field is designed to enhance the low-quality fingerprint image. The computational complexity is reduced by using a simple gradient-based method for estimating the orientation field and by using a small number of iterations. Although some of the broken ridges in the fingerprint image are partially connected after the first stage, this stage has a limitation of not being able to connect ridges broken with wide creases, and also not being able to recover ridges in the smeared regions. To overcome the shortcomings of the first stage, the fingerprint image obtained after the first-stage enhancement is passed through a compensation filter in the second stage. Although the broken ridges in the enhanced fingerprint image after the second stage are fully connected, the ridges affected by smears are only partially recovered. Hence, the output obtained from the second stage is passed through the third-stage enhancement, which has two phases: short-time Fourier transform (STFT) analysis and enhancement by an angular filter. In the first phase, a Gaussian spectral window is used in order to perform the STFT and this window helps to reduce the blocking effect in the enhanced image. In the second phase, the image obtained from the STFT is passed through an angular filter, which significantly improves the overall quality of the fingerprint image. In the second part of this thesis, the effectiveness and usefulness of the proposed enhancement scheme are examined in fingerprint feature extraction and matching for fingerprint recognition applications. For this purpose, a minutiae extraction algorithm is first applied to extract minutiae from fingerprint images and then a minutia-based matching algorithm is applied to the set of extracted minutiae using a hybrid shape and orientation descriptor in order to find similarity between a pair of fingerprints. Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases chosen from FVC2000, FVC2002 and FVC2004. Simulation results demonstrate not only the effectiveness of the proposed enhancement scheme in improving the subjective and objective qualities of fingerprint images, but also a superior minutiae extraction rate and a recognition accuracy of the fingerprint images enhanced by the proposed scheme at a reduced computational complexity

Microelectronics implementation of directional image-based fuzzy templates for fingerprints

Trabajo presentado al ICM celebrado en El Cairo del 19 al 22 de diciembre de 2010.Fingerprint orientation image, also called directional image, is a widely used method in fingerprint recognition. It helps in classification (accelerating fingerprint identification process) as well as in preprocessing or processing steps (such as fingerprint enhancement or minutiae extraction). Hence, efficient storage of directional image-based information is relevant to achieve low-cost templates not only for “match on card” but also for “authentication on card” solutions. This paper describes how to obtain a fuzzy model to describe the directional image of a fingerprint and how this model can be implemented in hardware efficiently. The CAD tools of the Xfuzzy 3 environment have been employed to accelerate the fuzzy modeling process as well as to implement the directional image-based template into both an FPGA from Xilinx and an ASIC.Peer Reviewe