Fingerprint Image Enhancement Using Directional Morphological Filter (original) (raw)
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Morphological enhancement and triangular matching for fingerprint recognition
2008
Among the principal problems for realizing a robust Automated Fingerprint Identification System (AFIS) there are the images quality and matching algorithms. In this paper a fingerprint enhancement algorithm based on morphological filter and a triangular matching are introduced. The enhancement phase is based on tree steps: directional decomposition, morphological filter and composition. For the matching phase a global transformation to overcame the effects of rotation, displacement and deformation between acquired and stored fingerprint is performed using the number of similar triangular, having fingerprint minutiae as vertexes. 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.
Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform
Nucleus, 2015
Fingerprints are the most renowned biometric trait for identification and verification. The quality of fingerprint image plays a vital role in feature extraction and matching. Existing algorithms work well for good quality fingerprint images and fail for partial fingerprint images as they are obtained from excessively dry fingers or affected by disease resulting in broken ridges. We propose an algorithm to enhance partial fingerprint images using morphological operations with region division technique. The proposed method divides low quality image into six regions from top to bottom. Morphological operations choose an appropriate Structuring Element (SE) that joins broken ridges and thus enhance the image for further processing. The proposed method uses SE βlineβ with suitable angle π and radius π in each region based on the orientation of the ridges. The algorithm is applied to 14 low quality fingerprint images from FVC-2002 database. Experimental results show that percentage accur...
Fingerprint Image Enhancement and Extraction of Minutiae and Orientation
Fingerprints are popular among the biometric β 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.
Blurred fingerprint image enhancement: algorithm analysis and performance evaluation
Signal, Image and Video Processing
The Automatic Fingerprint Matching (AFM) uses similarity score between an input and a reference fingerprint images to match fingerprints and the similarity score can be determined with a Minutiae Extraction Algorithm (MEA), which extracts minutiae of input and reference fingerprints. The performance of MEA depends on the quality of input fingerprint images. In case of blurred input fingerprint images, it becomes difficult to obtain a legitimate similarity score used in the AFM process. Therefore, an image enhancement algorithm must be incorporated with MEA to improve the performance of AFM process. In this study, good quality input fingerprint images have been intentionally blurred. Different enhancement algorithms are used to enhance the quality of blurred input fingerprint images. The performance of enhancement algorithms is analyzed and evaluated using the similarity score of extracted minutiae. Experimental results show that Volterra filter significantly enhances the quality of blurred input fingerprint images than other linear filters such as Laplacian and Gabor filters considered in this study.
A Study on Fingerprint Image Enhancement Techniques
β 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.
Fingerprint image enhancement: Algorithm and performance evaluation
Pattern Analysis and Machine β¦, 1998
A critical step in automatic ngerprint matching is to automatically and reliably extract minutiae from the input ngerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input ngerprint images. In order to ensure that the performance of an automatic ngerprint identi cation/veri cation system will be robust with respect to the quality of input ngerprint images, it is essential to incorporate a ngerprint enhancement algorithm in the minutiae extraction module. We present a fast ngerprint enhancement algorithm, which can adaptively improve the clarity of ridge and furrow structures of input ngerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online ngerprint veri cation system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the veri cation accuracy.
A REVIEW REPORT ON FINGERPRINT IMAGE ENHANCEMENT FILTER
TJPRC, 2013
This research aims to study diffrent kind of filter applied for fingerprint image enhancement. The fingerprint images which are obtained from sensors, scanning or imaging devices may be dirty, ink blurred or infected during acquisition process. The quality of a fingerprint image directly affects the performance of feature extraction and recognition system, so it is necessary to enhance fingerprint image by some method. Enhancement techniques are so varied, and use so many different image processing approaches, that it is difficult to assemble a meaningful body of techniques suitable for enhancement without extensive background development. For this reason an attempt is made to bring review on fingerprint enhancement filters and algorithms in this paper.
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 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.
Fingerprint Identification System using Tree Based Matching
International Journal of Computer Applications, 2012
With the increasing focus on the automatic personal identification applications, biometrics specifically fingerprint identification is the most reliable, secure and widely accepted technique. The automatic fingerprint identification systems have two important steps, such as fingerprint (a) image enhancement and (b) minutiae matching. In this paper, we develop a fingerprint image enhancement as well as matching algorithm based on directional curvature technique (DCT) of local ridges and a modified Tree based matching approach. In the preprocessing stage, the Fingerprint is De-noised, Binarised, Thinned and the approximate core points are calculated by DCT algorithm. The Minutiae points are extracted by template filtering over the image. Identifying all the minutiae accurately as well as rejecting false minutiae is another issue, addressed in this paper. The Minutiae Matching Score is determined using a modified Tree Matching algorithm with assigned probability value with its level priority. The study reveals that the proposed modified Tree Matching algorithm has better matching percentage for different fingerprints as well as low quality fingerprint image compared to the existing algorithms.