Spatial Filtering and Morphological Operation as Pre-Processing Steps in Fingerprint Feature Extraction (original) (raw)
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Fingerprint Verification based on Gabor Filter Enhancement
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Abstract— Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper we propose Fingerprint Verification based on Gabor Filter Enhancement (FVGFE) algorithm for minutiae feature extraction and post processing based on 9-pixel neighborhood. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms. Keywords-fingerprints; biometrics; ridge; orientation image; minutiae extraction. I.TRODUCTION I N The term Biometrics relates to the measurement (metric) of characteristics of a living (Bio) thing in order to identify a person. Biometric recognition is used as an automatic recognition of individuals based on the physiological or behavioral...
Fingerprint Enhancement using Gabor Filter Algorithm
Engineering and Scientific International Journal (ESIJ), 2018
The most important thing in fingerprint enhancement method is the segmentation of region and the interest. Many automatic systems for fingerprint enhancement techniques are based on minutiae matching concepts. Minutiae matching concept pointed to the core point, ridge ending and bifurcations of the ridges that identifies the fingerprint image pattern. The proposed enhancement method is an essential pre-processing of the fingerprint enhancement applications. Enhanced fingerprint is a common and complex in fingerprint identification method as it gives the base for the performance of computing system. The ridge structure in a fingerprint can be viewed as an orientation patterns, having a spatial frequency and orientation. The important step in fingerprint image recognition is the segmentation of the region of interest. Results define the robustness of the proposed system.
A Gabor filter-based approach to fingerprint recognition
… Processing Systems, 1999. SiPS 99. 1999 …, 1999
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An enhanced Gabor filter-based segmentation algorithm for fingerprint recognition systems
ISPA 2005: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005
An important step in fingerprint recognition is the segmentation of the region of interest. In this paper, we present an enhanced approach for fingerprint segmentation based on the response of eight oriented Gabor filters. The performance of the algorithm has been evaluated in terms of decision error trade-off curves of an overall verification system. Experimental results demonstrate the robustness of the proposed method.
FINGERPRINT IMAGE ENHANCEMENT AND FEATURE EXTRACTION USING AFE, GABOR FILTER AND FIXED TEMPLATES
Abstract: Finger print verification system is the most trustable biometric system in the world. However, finger print matching, especially when the finger print images have low quality or when the matching is performed cross-sensors, is still an open research. The main problem in automatic finger print identification is to acquire reliable features from finger print image with poor quality. So there is a urgent need for finger print image enhancement and Feature extraction. This project proposes a method for finger print image enhancement and feature extraction for low quality finger prints. This project provides a adaptive fingerprint image enhancement method which automatically adjust with parameters based on the input finger print image. The pre-processing method includes global and local analysis for better enhancement. The contrast can be enhanced to provide better visual enhancement using SMQT method. The input low quality fingerprint image should be enhanced and finally it would be segmented in to a binarized mode using Gabor filter. The feature extraction is done using fixed templates. The main modules on the project are Pre-processing using Adaptive Finger Enhancement (AFE), Ridge Segmentation and Feature extraction. This paper can be helpful to increase the verification accuracy on AFIS(Automatic Fingerprint Identification System). Keywords: Adaptive Finger Enhancement (AFE), Gabor filter, AFIS, SMQT. Title: FINGERPRINT IMAGE ENHANCEMENT AND FEATURE EXTRACTION USING AFE, GABOR FILTER AND FIXED TEMPLATES Author: Mrs. G. Sangeetha Lakshmi, Ms. A.Sivasankari, Ms.V.Punitha International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online), ISSN 2348-1196 (print) Research Publish Journals
Gabor Filter-based Multiple Enrollment Fingerprint Recognition
International Journal of Computer Applications, 2016
Minutiae-based matching techniques have been widely used in the implementation of multiple enrollment fingerprint recognition systems. However, these techniques suffer the difficulty of automatically extracting all minutiae points due to failure to detect the complete ridge structures of a fingerprint. With poor quality fingerprint images, detection of minutiae points as well as describing all the local ridge structures is difficult. It is also difficult to quickly match two fingerprints that have a difference in the number of unregistered minutiae. Non-minutiae based techniques such as Gabor filtering are rich in terms of distinguishing features and can be used as an alternative since they capture both the local and global details in a fingerprint. This paper presents a Gabor filter-based approach; the first of the kind to implement a verification multiple enrollment based fingerprint recognition system. The Gabor filter-based multiple enrollment fingerprint recognition method was compared with a spectral minutiae-based method using two fingerprint databases; FVC 2000-DB2-A and FVC 2006-DB2-A. Although the minutiaebased method outperformed the Gabor filter-based method, the results attained from the later are promising and can be a good basis for implementing Gabor filter-based techniques in designing multiple enrollment based fingerprint systems.
FINGERPRINT DETECTION AND RECOGNIZATION TECHNIQUES USING GABOR FILTER
Fingerprints are most extensively and effectively appropriate for the proof of identity in present days. Mostly because of their uniqueness among the people, public acceptance, originality, stability through life, and their least risk of invasion. Fingerprint technology, which is basically a biometric system, is utilized to identify an individual based on their physical qualities. Fingerprint matching is the trendiest biometric method appropriate to provide authentication. Fingerprint verification is one of the most trustable biometric security system in the world of computers. In this paper we proposed fingerprint algorithm which uses Gabor filter to capture local and global minute details in a fingerprint. The matching is based Euclidean distance between input image to test compared to trained fingerprints images. We are able to detect fingerprint with marginally best results. Our fingerprint framework performs best as compared to any other techniques.
Fingerprint Recognition Using Gabor Filter with Neural Network
2014
The automated classification and matching of fingerprint images has been a challenging problem in pattern recognition over the past decades. This paper proposes a method to detect the rotation region based on Estimate Global Region (EGR) that has the maximum rotation region. The Gabor filter based feature is applied for extracting fingerprint features from gray level images without preprocessing. The fingerprint recognition is developed by neural networks with adaptive learning rate. The paper contains a co mparison between using EGR algorithm and without using EGR. The Gabor filter without EGR gives the best result for the fingerprint recognition with outrotation while the rotation of the fingerprint with angles (5 o ,10 o and 20 o ) gives worse results in finger print recognition. The proposalmethod gives best result in rotation the fingerprint image with and without the rotation of the same angles. The result of the correlation for the proposalmethod is 99%.
with identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filterbased algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. The fingerprint matching is based on the Euclidean distance between the two corresponding Finger Codes and hence is extremely fast.