Fingerprint Image Enhancement and Extraction of Minutiae and Orientation (original) (raw)
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Image enhancement and minutiae matching in fingerprint verification
Pattern Recognition Letters, 2003
Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we develop a fingerprint image enhancement algorithm based on orientation fields; According to the principles of Jain et al.Õs matching algorithm, we also introduce ideas along the following three aspects: introduction of ridge information into the minutiae matching process in a simple but effective way, which solves the problem of reference point pair selection with low computational cost; use of a variable sized bounding box to make our algorithm more robust to non-linear deformation between fingerprint images; use of a simpler alignment method in our algorithm. Experiments using the Fingerprint Verification Competition 2000 (FVC2000) databases with the FVC2000 performance evaluation show that these ideas are effective.
Fingerprint Image Enhancement and Minutia Extraction
Fingerprints are the oldest and most widely used form of biometric identification. Despite the widespread use of fingerprints, there is little statistical theory on the uniqueness of fingerprint minutiae. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images.
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 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.
IJERT-An Approach To Extract Minutiae Points From Enhanced Fingerprint Image
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/an-approach-to-extract-minutiae-points-from-enhanced-fingerprint-image https://www.ijert.org/research/an-approach-to-extract-minutiae-points-from-enhanced-fingerprint-image-IJERTV2IS2544.pdf Fingerprints are the most widely used method of personal identification of any person .The performance of fingerprint recognition system highly depends on the quality of acquired image. And it works well if the image is of good quality. This paper presents an approach that first enhance the fingerprint image and then extract the minutiae features i.e. ridges and bifurcations .For the enhancement of the images this approach uses the histogram equalization and some filters .After the enhancement of the image the minutiae features are extracted that are used for matching of fingerprints.
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.
NOVEL APPROACHES OF BIOMETRIC FINGER PRINT MINUTIAE DETECTION AND EXTRACTION PROCESS
The most common use of biometric identification method is fingerprint recognition. Fingerprints are unique for every person. Biometric Fingerprint identification has immense in forensic science & criminal investigations. The automatic fingerprint recognition systems are based on local ridge features called as minutiae. Minutiae are automatic identification systems based on ridge bifurcations and terminations. Hence it is extremely important to mark these minutiae accurately and reject the false ones. However, prone to degradation and corruption of fingerprint images due to certain factors such that skin variations and impression such as dirt, humidity, scars and non-uniform. We should apply some image enhancement techniques before minutiae extraction.
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.