Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform (original) (raw)

Improved Technique for Fingerprint Segmentation

a valley is the region between two adjacent ridges. The minutiae, which are the local discontinuities in the ridge flow pattern, provide the features that are used for identification. Details such as the type, orientation, and location of minutiae are taken into account when performing minutiae extraction . Galton [5] defined a set of features for fingerprint identification, which since then, has been refined to include additional types of fingerprint features. How-ever, most of these features are not commonly used in fingerprint identification systems. Instead the set of minutiae types are restricted into only two types, ridge endings and bifurcations, as other types of minutiae can be expressed in terms of these two feature types. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. occur due to variations in skin and impression conditions such as scars, humidity, dirt, and non-uniform contact with the fingerprint capture device . Thus, image enhancement techniques are often employed to reduce the noise and enhance the definition of ridges against valleys.

Fingerprint Image Enhancement Using Directional Morphological Filter

EUROCON 2005 - The International Conference on "Computer as a Tool", 2005

Fingerprint images quality enhancement is a topic phase to ensure good performance in an Automatic Fingerprint Identification System (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on tree steps: directional decomposition, morphological filter and composition. The performance of the proposed approach has been evaluated on two set of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner.

Fingerprint Image Enhancement: Segmentation to Thinning

2012

Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement.

Fingerprint Image Enhancement and Extraction of Minutiae and Orientation

Fingerprints are popular among the biometric – based systems due to ease of acquisition, uniqueness and availability. Fingerprint based biometric systems work by extracting and matching some features on the fingerprint. Due to errors in acquisition phase, it is possible that the scanned fingerprint image is not of a good quality and hence needs to be enhanced before being processed by the feature extracting module. Out of the various features that can be extracted, orientation and minutiae points are the most common ones to be used. This paper discusses some commonly used fingerprint enhancement techniques, the algorithms for minutiae and orientation extraction followed by the comparison of the algorithm on various databases.

Extracting and Enhancing the Core Area in Fingerprint Images

An automatic fingerprint identification system (AIFS) plays a major role in forensic applications such as criminal identification, ATM card verification, etc. The fingerprint enhancement is essential in the issues of AIFS to ensure robust performance. This paper describes the design and implementation of fingerprint enhancement in two stages: extraction of high ridge curvature area (core) and enhancing the core block. This enhancement algorithm is concentrated on enhancing the block, around the core block for two reasons 1) Rich minutiae exist close around the core point. 2) Absence of delta point in certain fingerprint images. This feature of enhancing core block is essential for fast and robust performance of fingerprint verification / identification. The high ridge curvature area is extracted using the local ridge orientation and the enhancement is based on the estimated local ridge orientation and frequency. The system has been tested on a variety of fingerprint images even of very poor quality and the results showed remarkable performance. Experimental results showed that fingerprint enhancement algorithm is best suited for the verification with high accuracy. The complete fingerprint enhancement procedure takes on an average of about three seconds which is remarkably good.

Uneven Background Extraction And Segmentation Of Good, Normal And Bad Quality Fingerprint Images

2006 International Conference on Advanced Computing and Communications, 2006

In this paper, we have considered a problem of uneven background extraction and segmentation of good, normal and bad quality fingerprint images, though we propose an algorithm based on morphological transformations. Our result shows that the proposed algorithm can successfully extract the background of good, normal and bad quality images of fingerprint and well segment the foreground area. The algorithm has been tested and executed on FVC2002 database and the performance of proposed algorithm is evaluated through subjective and objective quality measures. This algorithm gives good and promising result and found suitable to remove superfluous information without affecting the structure of fingerprint image as well as reduces the storage space for the resultant image upto 77%. Our results will be useful for precise feature extraction in automatic fingerprint recognition system.

A Study on Fingerprint Image Enhancement Techniques

– Fingerprints have ridges and valleys on the surface of the finger. Segments on the top skin layer are the ridges and the bottom skin layers are valleys. Minutia points are designed by ridges. The fingerprint is identified uniquely by the pattern of the ridges and minutiae points. There are 5 categories of patterns available in a fingerprint: arch, tented arch, left loop, right loop and whorl. Sensor captures several images of finger under different Illumination conditions that include different wavelengths, different illumination orientations, and different polarization conditions. The output contains information about both the surface and subsurface features of the skin. The finger print image used for matching must be of good quality and it must be without of any type of noise. Reduce the amount of noise in finger print image gives more accurate results. Reducing noise in finger print image is not an easy process. Because of this the fingerprint image gives inopportune minutiae results. Therefore the fingerprints must be improved to mine the minutiae and get entire features of the fingerprints. There have been different image enhancement technique approaches and filters were developed to enhancement the fingerprint images. There are three main techniques of enhancement. Pixel wise Enhancement Techniques, Contextual Filter Enhancement Techniques and Multi Resolution Enhancement Techniques. This paper focuses on these various Fingerprint Enhancement Techniques.

A comparison of fingerprint enhancement algorithms for poor quality fingerprint images

2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014

ABSTRACT Image enhancement is a common step in most fingerprint feature extraction algorithms. Unfortunately, many fingerprints are poor in quality, which makes extracting reliable minutiae difficult. There are many factors why this may be the case. There might be too much noise or ghosting on the image, or damages (such as scars or creases) on the fingers itself caused by the person's line of work, such as secretaries who deal with a lot of paper in their job, or people who perform manual labor as their occupation. This research attempts to evaluate the effectiveness of image enhancement by comparing three different algorithms, including the use of power transformation in the frequency domain, smoothing on the spatial domain and contextual filtering using Gabor Filters. The experimental results definitely showed improvements after enhancing poor quality fingerprint images, especially when the image is processed in the frequency domain. Contextual filtering also works well in enhancing images based on data in the local context, but in order for it to be more effective and be able to construct better enhanced fingerprint image results, it should also be accompanied with data in the global context.

Morphological and gradient based fingerprint image segmentation

2011 International Conference on Information and Communication Technologies, 2011

For personal identification the use of fingerprint identification systems is mostly common. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. Extraction of region of interest (ROI) from the desired fingerprint impression is the main purpose of segmentation. In this paper, we present a new technique for fingerprint segmentation using morphological operations and modified gradient based technique. The distinct feature of our technique is that it gives high accuracy for fingerprint segmentation even for low quality fingerprint images. The proposed algorithm is applied on standard fingerprint databases, FVC2002 and FVC2004. Experimental results demonstrate the improved performance of the proposed scheme.