A Novel and Efficient Algorithm of Textural Feature Extraction for Fingerprint Identification (original) (raw)
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IJERT-A Novel and Efficient Algorithm of Textural Feature Extraction for Fingerprint Identification
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/a-novel-and-efficient-algorithm-of-textural-feature-extraction-for-fingerprint-identification https://www.ijert.org/research/a-novel-and-efficient-algorithm-of-textural-feature-extraction-for-fingerprint-identification-IJERTV1IS5090.pdf With the need of automatic personal identification and their extensive use in forensics, fingerprints are receiving a lot of attention. However numerous fingerprint systems currently available still do not meet performance requirement of several civilian applications as they use traditional approach for fingerprint recognition. There is a major disadvantage of traditional approach, as they do not actually utilize the rich discriminatory information contained in fingerprint. Thus trying to eliminate this shortcoming we present an improved & efficient approach for fingerprint recognition providing accurate automatic personal identification. Here we extract the local ridge features of fingerprint image by a bank of 16 Gabor filters divided in squared blocks. The matching is based on Euclidian distance between Finger codes. Hence this approach make use of orientation and frequency of local ridge structure which is the rich discriminatory information in fingerprint, secondly square tessellation covers the entire image and Euclidian distance based matching of finger codes increases the speed of matching process.
Fingerprint Verification based on Gabor Filter Enhancement
2009
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 Verification Using the Texture of Fingerprint Image
2009 Second International …, 2009
In this paper, a new fingerprint verification algorithm is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to poor image quality. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point using the orientation reliability and then extract a 129 X 129 block, making the reference point its center. From the 16 co-occurrence matrices, four statistical descriptors are computed. The experimental results have been analyzed using FVC testing protocol; the equal error rate (EER) is 0.32%. Furthermore, the comparison with other methods shows that the proposed method is more accurate and robust for reliable fingerprint verification.
Fingerprint Matching using Ridge Patterns
Atherosclerosis, 2005
This paper presents a fingerprint matching scheme that utilizes a ridge patterns to match fingerprint images. The proposed scheme uses a set of 16 Gabor filters where spatial frequencies correspond to the average inter-ridge spacing in fingerprints. It is used to capture the ridge strength at equally spaced orientations. A circular tessellation of filtered image is then used to construct
Personal Identification by Fingerprints based on Gabor Filters
2009
This work is released in biometric field and has as goal, development of a full automatic fingerprint identification system using methods which have been tested in LAMOSI laboratory. Promising Results of first experiences using radial basis function neural network and support vector machine pushed us to continue the exploitation of new version of neural networks which is spike neural networks and to develop codification and recognition algorithms which are specifically associated to this system. In this context, works were consecrated on algorithm developing of the original image processing, minutiae and singular points localization; Gabor filters coding and testing these algorithms on well known databases such as: FVC2004 databases & FingerCell database. Performance Evaluating has proved that spike neural network achieved a good recognition rate closer to rates achieved by other methods but in a very short time and this make it more useful in online applications.
MULTIPLE FEATURES BASED FINGERPRINT IDENTIFICATION SYSTEM
Security has become major issue now a day. In order to prevent unauthorized access of confidential data there is a need for accurate and reliable personal identification system. So, biometric based identification system is one of the best solutions. Fingerprint based system is one of oldest biometric identification systems. It is used in many commercial and security applications. Even with advent of technology in fingerprint identification system, the accurate extraction and matching of features from a fingerprint image is a challenging task. The task is much more challenging when fingerprint is affected by non-linear deformations such as rotation and translation. In this paper, fingerprint identification system using improved feature vector based algorithm is presented. In the algorithm Gabor filter is implemented to enhance the fingerprint image. The salient features minutiae (ridge endings) and reference point are extracted from the image. The Euclidian distances between reference point and each minutiae point are calculated and are arranged in ascending order. These are stored in database as feature vectors. The fingerprint matching is done based on the similarity rate between the feature vector of input fingerprint and the feature vectors stored in the database. Algorithms are implemented using Visual Studio 2010 in C++ language using Open CV libraries and tested on the fingerprint database created in the laboratory.
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.
A Gabor filter-based approach to fingerprint recognition
… Processing Systems, 1999. SiPS 99. 1999 …, 1999
We propose a Gabor-Alter-based method for fingerprint recognition in this paper. The method makes use of Gabor filtering technologies and need only to do the core point detection before the feature extraction process without any other pre-processing steps such as smoothing, binarization, thinning, and minutiae detection. The proposed Gabor-filter-based features play a central role in the processes of fingerprint recognition, including local ridge orientation, core point detection, and feature extraction. Experimental results show that the recognition rate of the k-nearest neighbor classifier using the proposed features is 97.2% for a small-scale fingerprint database, and thus that the proposed method is an efficient and reliable approach.
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.