Fingerprint Image Enhancement and Minutia Extraction (original) (raw)
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Minutiae Extraction from Fingerprint Images a Review
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.
Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction
International Journal of Advanced Research in Computer Science and Software Engineering, 2018
Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The results achieved are compared with those obtained through some other methods. The Results show some improvement in the minutiae extraction in terms of quantity.
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.
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.
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 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.
Designing of Fingerprint Recognition System Using Minutia Extraction and Matching
2015
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. This work deals with the development of a highly robust and efficient biometric person identification system based on fingerprint features. Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint verification through extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, pre-processing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. Many methods have been combined to build a minutia extractor and a minutia matcher. Minutia-marking with false minutiae removal methods are used in the work. An alignmentbased elastic matching algorithm has been developed for minutia matching. This algorithm is c...
A New Algorithm for Locating and Extracting Minutiae from Fingerprint Images
Pattern Recognition and Image Analysis, 2019
Fingerprints are considered as the oldest and most widely used in the world for biometric identification. Every person has unique and permanent fingerprints. Most automatic fingerprint recognition systems are based on features formed from lines known as minutiae. Building a database of unique minutiae is very important in the security systems because it concerns the identification of the person committing a crime through the latent left in the crime scene. This research presents a new algorithm to give a minutia a unique value leading to accelerating the search process for a person. The algorithm splits the fingerprint image into four sections, then calculates the values for each minutia in each section and stores it in a database that is designed for this purpose. This research provides a great opportunity and additional options to fingerprint experts in order to solve many cases that are still undiscovered while searching for the latent in the database of minutiae. The results of testing this algorithm were very successful, very encouraging and helpful to fingerprint experts in their work.