Online Fingerprint Verification Algorithm and Distributed System
26 May 2011-Journal of Signal and Information Processing (Scientific Research Publishing)-Vol. 2, Iss: 2, pp 79-87
TL;DR: A novel online fingerprint verification algorithm and distribution system that is insensitive to fingerprint image distortion, scale, and rotation, and robust even on poor quality fingerprint images is proposed.
Abstract: In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastic structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.
Citations
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TL;DR: A fast fingerprint verification algorithm using level-2 minutiae and level-3 pore and ridge features using a feature supervector yields discriminatory information and higher accuracy compared to existing recognition and fusion algorithms.
Abstract: This paper presents a fast fingerprint verification algorithm using level-2 minutiae and level-3 pore and ridge features. The proposed algorithm uses a two-stage process to register fingerprint images. In the first stage, Taylor series based image transformation is used to perform coarse registration, while in the second stage, thin plate spline transformation is used for fine registration. A fast feature extraction algorithm is proposed using the Mumford-Shah functional curve evolution to efficiently segment contours and extract the intricate level-3 pore and ridge features. Further, Delaunay triangulation based fusion algorithm is proposed to combine level-2 and level-3 information that provides structural stability and robustness to small changes caused due to extraneous noise or non-linear deformation during image capture. We define eight quantitative measures using level-2 and level-3 topological characteristics to form a feature supervector. A [email protected] vector machine performs the final classification of genuine or impostor cases using the feature supervectors. Experimental results and statistical evaluation show that the feature supervector yields discriminatory information and higher accuracy compared to existing recognition and fusion algorithms.
34 citations
Cites background or methods from "Online Fingerprint Verification Alg..."
...The details of the minutiae extraction and matching algorithms are found in [9,10], respectively....
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...Performance evaluation of level-3 feature extraction algorithm: We first compute the verification performance of the proposed level-3 feature extraction algorithm and compare it with existing level-2 [9,10] and level-3 feature based verification algorithms [4,5,7]....
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...The extracted minutiae are matched using a dynamic bounding box based matching algorithm [10]....
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01 Jun 2010
TL;DR: This paper has developed and tested structuring elements for different types of minutiae present in a fingerprint image to be used by the HMT after preprocessing the image with morphological operators, which results in efficientminutiae detection, thereby saving a lot of effort in the post processing stage.
Abstract: Fingerprints are the most widely used parameter for personal identification amongst all biometrics based personal authentication systems. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is critically important. In this paper we propose an algorithm for extracting minutiae from a fingerprint image using the binary Hit or Miss transform (HMT) of mathematical morphology. We have developed and tested structuring elements for different types of minutiae present in a fingerprint image to be used by the HMT after preprocessing the image with morphological operators. This results in efficient minutiae detection, thereby saving a lot of effort in the post processing stage. The algorithm is tested on a large number of images. Experimental results depict the effectiveness of the proposed technique.
31 citations
Cites background or methods from "Online Fingerprint Verification Alg..."
...There are about 150 different types of minutiae [2] categorized according to their configuration....
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...Many algorithms [2, 16, 26, 27, 28] have been proposed in the literature for minutia analysis and fingerprint classification for better fingerprint verification and identification....
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04 Jan 2010
TL;DR: The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance and fingerprint recognition algorithms provide comparatively better performance on urban population.
Abstract: This paper presents a feasibility study to compare the performance of fingerprint recognition on rural and urban Indian population. The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance. On the other hand, fingerprint recognition algorithms provide comparatively better performance on urban population. The study also shows that poor images quality, worn and damaged patterns and some special characteristics affect the performance of fingerprint recognition.
20 citations
Cites methods from "Online Fingerprint Verification Alg..."
...– Implementation of minutiae feature extraction and matching [2] termed as Algorithm - A – Implementation of filterbank based feature extraction and matching [3] termed as Algorithm - B – Two commercial fingerprint recognition systems, termed as System - C and System - D(1)...
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TL;DR: The proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints, and multiple criteria such as first-order, second-order and third-order features are used to classify the overlaps latent fingerprints.
Abstract: Latent fingerprints have attracted considerable attention from researchers in the fields of forensics and law enforcement applications. Public demand for these applications may be the driving force behind further progress in biometrics research. Although great effort has been taken to devise algorithms for overlapped latent fingerprint classification system, there are still many challenging problems involved in fingerprint classification systems. Most of the fingerprint-based applications will prolong with fingerprint recognition because of its proven performance, the existence of large databases and the availability of the fingerprint devices with minimum cost. There are various issues that need to be addressed to develop fingerprint classification system. In this connection, there are some designing challenges such as nonlinear distortion, low-quality image, segmentation, sensor noise, skin conditions, overlapping, inter-class similarity, intra-class variations and template ageing. In crime scenes, the latent images can be overlapped with some background images or more number of fingerprint images from same person or different person. An overlapped fingerprint image should be processed for fingerprint classification. This proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints. The proposed multiple criteria such as first-order, second-order and third-order features are used to classify the overlapped latent fingerprints. The proposed system designs the novel classification system for overlapped latent fingerprint images using ANFIS classifier. Extensive experiments are performed on the simultaneous latent fingerprint databases, and National Institute of Standards and Technology-Special Database 27, Fingerprint Verification Competition 2006 Database1-A and Database2-A databases. The planned work enables accurate and fast data retrieval by using one-to-N fingerprint classification for overlapped latent images. The experimental results are highly promising, and they outperform the existing systems in classifying overlapped images. The performance of adaptive neuro fuzzy inference system classifier is evaluated by applying the k-fold cross-validation technique. The outcome of the work shows that the overlapped fingerprint is classified in a successful manner, and the results are compared with Bayes, SVM and MLP classifiers. The obtained results show that the proposed system achieves a better classification rate of 90.66% with 5 s, 86.66% with 12 s compared to the existing system.
9 citations
Dissertation•
01 Jan 2012
TL;DR: The proposed algorithm introduces a novel approach to memory distribution of block-wise image processing operations and discusses three different ways to process pixels along the partitioning axes of the distributed images.
Abstract: Biometric systems such as face, palm and fingerprint recognition are very computationally expensive. The ever growing biometric database sizes have posed a need for faster search algorithms. High resolution images are expensive to process and slow down less powerful extraction algorithms. There is an apparent need to improve both the signal processing and the searching algorithms. Researchers have continually searched for new ways of improving the recognition algorithms in order to keep up with the high pace of the scientific and information security world. Most such developments, however, are architectureor hardware-specific and do not port well to other platforms. This research proposes a cheaper and portable alternative. With the use of the Single Program Multiple Data programming architecture, a distributed fingerprint recognition algorithm is developed and executed on a powerful cluster. The first part in the parallelization of the algorithm is distributing the image enhancement algorithm which comprises of a series of computationally intensive image processing operations. Different processing elements work concurrently on different parts of the same image in order to speed up the processing. The second part of parallelization speeds up searching/matching through a parallel search. A database is partitioned as evenly as possible amongst the available processing nodes which work independently to search their respective partitions. Each processor returns a match with the highest similarity score and the template with the highest score among those returned is returned as match given that the score is above a certain threshold. The system performance with respect to response time is then formalized in a form of a performance model which can be used to predict the performance of a distributed system given network parameters and number of processing nodes. The proposed algorithm introduces a novel approach to memory distribution of block-wise image processing operations and discusses three different ways to process pixels along the partitioning axes of the distributed images. The distribution and parallelization of the recognition algorithm gains up to as much as 12.5 times performance in matching and 10.2 times in enhancement.
3 citations
References
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TL;DR: A fast parallel thinning algorithm that consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deletion thenorth-west boundarypoints and theSouth-east corner points.
Abstract: A fast parallel thinning algorithm is proposed in this paper It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points End points and pixel connectivity are preserved Each pattern is thinned down to a skeleton of unitary thickness Experimental results show that this method is very effective 12 references
2,243 citations
TL;DR: An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner and an alignment-based elastic matching algorithm has been developed.
Abstract: Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.
1,376 citations
TL;DR: A filter-based fingerprint matching algorithm which uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode and is able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature.
Abstract: 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 a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
1,207 citations
TL;DR: The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search and incorporates elastic structural feature-based matching for indexing the database at the lowest level.
Abstract: With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain.
725 citations
TL;DR: An effective fingerprint verification system is presented, which assumes that an existing reference fingerprint image must validate the identity of a person by means of a test fingerprint image acquired online and in real-time using minutiae matching.
Abstract: An effective fingerprint verification system is presented. It assumes that an existing reference fingerprint image must validate the identity of a person by means of a test fingerprint image acquired online and in real-time using minutiae matching. The matching system consists of two main blocks: The first allows for the extraction of essential information from the reference image off-line, the second performs the matching itself online. The information is obtained from the reference image by filtering and careful minutiae extraction procedures. The fingerprint identification is based on triangular matching to cope with the strong deformation of fingerprint images due to static friction or finger rolling. The matching is finally validated by dynamic time warping. Results reported on the NIST Special Database 4 reference set, featuring 85 percent correct verification (15 percent false negative) and 0.05 percent false positive, demonstrate the effectiveness of the verification technique.
376 citations