Fingerprint Image Enhancement Based on Various Techniques, Feature Extraction and Matching (original) (raw)
Related papers
Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae. The techniques are broadly classified as those working on binarized images and those that work on gray scale images directly.
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.
IRJET-Fingerprint Image Enhancement Based on Various Techniques, Feature Extraction and Matching
Exact involuntary individual recognition is critical in a diversity of applications in our electronically organized society. Biometrics, which mentions to recognition based on physical or behavioral characteristics, is being increasingly adopted to give positive recognition with a high degree of confidence. Among all biometric techniques, fingerprint-based authentication schemes have established most attention because of long history of fingerprints and their general apply in forensics. Fingerprints are a great source for recognition of individuals. Fingerprint recognition is one of the forms of biometric recognition. However obtaining a decent fingerprint image is not always easy. So, fingerprint image should be pre-processed by matching. The main objective of this work is to propose an image matching algorithm which is useful to every image for matching. For professional enhancement and feature extraction procedures, the segmented structures should be invalid of every noise. A pre-processing method containing of field course, ridge frequency estimated, filtering, partition and enhancement is performed. The attained image is useful to a thinning algorithm and following minutiae removal. The association of image pre-processing and minutiae extraction is deliberated. The simulations are performed in the MATLAB atmosphere to estimate the performance of the implemented algorithms. MATLAB provides a valuable atmosphere for these progresses. Outcome and interpretation of the fingerprint images
Fingerprint Image Processing for Automatic Verification
1999
The performance of an automatic fingerprint verification approach relies heavily on the quality of the fingerprint image. Enhancement of the fingerprint image is then a crucial step in automatic fingerprint verification. This paper discusses the fingerprint image processing methods for automatic verification and proposes an adaptive oriented low pass filter to enhance the fingerprint image quality. For automatic fingerprint verification the fingerprint image processing is not aimed at improving the visual appearance of the fingerprint image but aimed at facilitating the subsequent processing. Therefore, the fingerprint image processing method is closely related to the method employed for the subsequent minutiae detection. The proposed approach takes efforts to increase the chances for success of the subsequent processes and at the same time avoid producing undesired side effects such as losing of the original ridge structure information or introducing additional spurious ridge structure information. Some sample results are given to illustrate the performance of the proposed approach.
An algorithm for fingerprint image postprocessing
2000
Most of the current fingerprint identification and verification systems perform fingerprint matching based on different attributes of the minutia details present in fingerprints. The minutiae (i.e. ridge endings and ridge bifurcations) are usually detected in the thinned binary image of the fingerprint. Due to the presence of noise as well as the use of different preprocessing stages the thinned binary image contains a large number of false minutiae which may highly decrease the matching performances of the system. A new algorithm of fingerprint image postprocessing is proposed in this paper The algorithm operates onto the thinned binary image of the fingerprint, in order to eliminate the false minutiae. The proposed algorithm is able to detect and cancel the minutiae associated with most of the false minutia structures which may be encountered in the thinned fingerprint image.
A Study on Fingerprint (biometrics) Recognition
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge. I. INTRODUCTION
Review on Fingerprint Recognition
—Biometric system works on behavioral and physiological biometric parameters to spot a person. Every fingerprint contains distinctive options and its recognizing system primarily works on native ridge feature local ridge endings, minutiae, core point, delta, etc. However, fingerprint pictures have poor quality thanks to variations in skin and impression conditions. In personal identification, fingerprint recognition is taken into account the foremost outstanding and reliable technique for matching with keep fingerprints within the information. Minutiae extraction is additional essential step in fingerprint matching. This paper provides plan regarding numerous feature extraction and matching algorithms for fingerprint recognition systems and to seek out that technique is additional reliable and secure. Keywords— fingerprint images, minutiae extraction, ridge endings, ridge bifurcation, fingerprint recognition.
Fingerprint Image Enhancement and its Feature Extraction for Recognition
ijstr.org
Fingerprint recognition is one of the most popular and successful methods used for person identification, which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae; which are points where a curve track finishes, intersect with other track or branches off. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations. The goal of this paper is to represent a complete process of fingerprint feature extraction for minutiae matching.
Fingerprint Enhancement and its features purification
It is difficult to extract only genuine minutiae from fingerprints. Enhancement techniques are being used as preprocessing methods for minutiae extraction. The fingerprint enhancement is a challenging process. To overcome the adverse effect caused by spurious minutiae in fingerprint matching, a new method for fingerprint enhancement is proposed. Since some of the spurious minutiae in the boundary region cannot be remo ve in the minutiae purification, we draw a region of interest in the fingerprint image to remove the remaining boundary minutiae which exists as a ridge ending. These boundary minutiae affect the accuracy in fingerprint matching. Experimental result shows that the proposed method can eliminate the effect caused by spurious minutiae.