A Framework for Efficient Fingerprint Identification Using a Minutiae Tree

Mansukhani, Praveer; Tulyakov, Sergey; Govindaraju, Venu

doi:10.1109/jsyst.2009.2037286

Cited by 28 publications

(10 citation statements)

References 8 publications

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“…Our algorithm is the modified version of the algorithm proposed in (Mansukhani et al, 2010) which uses a tree structure in which the fingerprint templates are enrolled at the leaf nodes. Mansukhani et al (2010) an arbitrary minutiae is selected as the root of the tree and the features of the nearest minutiae relative to the root are calculated and the particular fingerprint is enrolled at the leaf node. The size of the tree will be very higher since one fingerprint is enrolled in many leaves.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For matching two fingerprints, the kplets are matched by local matching and the local matches are combined by a global coupled breadth first search (Chikkerur et al, 2006). The same idea has been used for indexing in (Mansukhani et al, 2010). In this approach, the fingerprints are enrolled as a global tree and for matching, the probe is matched against the tree.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to the single path search, multiple path search takes more time as it need to search multiple paths. The running time of the algorithm is compared with previously proposed algorithm (Mansukhani et al, 2010). The comparison of the average running time for single search path and multiple path search when the depth of the tree increases is given in Table 2 and 3.…”

Section: Running Timementioning

confidence: 99%

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Enrolment and Matching of Fingerprints using Minutiae Tree

Praseetha¹,

Vadivel²

2019

Journal of Computer Science

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Automated fingerprint matching is considered as the most challenging phase of fingerprint recognition since it can be affected by a variety of factors such as noise, skin condition, rotation, distortions and displacements. When there is a large database, the search time to get a matching fingerprint will be relatively high. To reduce the searching time in the database, we have proposed a minutiae tree based indexing method in this paper. The database is represented in the form of a minutiae tree and the fingerprint matching is done by visiting the nodes in the tree where the local configuration of the minutiae is stored. By using this tree structure it is found that the search time or matching time can be considerably reduced and the matching time is independent of the number of fingerprints enrolled in the database. Our framework is scalable and the experiments conducted explores its ability to find correct matches with minimal search time.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Running Timementioning

confidence: 99%

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Enrolment and Matching of Fingerprints using Minutiae Tree

Praseetha¹,

Vadivel²

2019

Journal of Computer Science

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“…A poor quality fingerprint image is low in divergence, noisy, exhausted, or smudgy, causing spurious and missing minutiae. Poor quality can be due to cuts, crinkles, or bruises on surface of fingertip, excessively wet or dry skin condition, uncooperative attitude of subjects, broken and impure scanner devices, little quality fingers (elderly people, manual worker), and other factors [11].…”

Section: Amentioning

confidence: 99%

Design & Implementation of Fingerprint Biometrics based on Discretized Fingerprint Texture Descriptor

Kamboj¹

2015

IJIRCCE

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Accurate automatic personal identification is critical in a variety of applications in our electronically interconnected society. So, this paper presents a fingerprint biometric system based on texture descriptors. For this, it requires an image of high PSNR value so that complete features must be extracted and all matching points will be obtained. For this, it uses an contrast based enhancement algorithm for improving PSNR value. We have considered the factors relating to obtaining high performance feature points detection algorithm, such as image quality, separation, image improvement and feature detection. Commonly used features for increasing fingerprint image quality are features vectors and local orientation. Accurate separation of fingerprint ridges from noisy background is necessary. A pre-processing method containing of field orientation, frequency estimation, filtering, segmentation and enhancement is performed. Image normalization is also done for equalizing the features values. Also area of interest is also found out. All simulations are done in MATLAB tool.

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“…The extracted information is used as an index into a hash table. Another indexing method based on minutiae was proposed by Mansukhani et al [18]. They constructed a large minutiae index tree, where the enrolled templates are represented by the leaves of the tree.…”

Section: Related Workmentioning

confidence: 99%

Classification of Extended Delaunay Triangulation for Fingerprint Indexing

Kavati

Chenna

Prasad

et al. 2014

2014 8th Asia Modelling Symposium

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In this paper, we propose an efficient indexing mechanism for the fingerprints based on minutiae triplets. First, we compute the Delaunay triangulation for each fingerprint using the minutiae features. Further, we define a novel representation for the fingerprint named extended triangle set, which is very tolerant to missing minutiae. The defined extended triangle set is an extension of the Delaunay triangulation and is used in combination with a classification approach to narrow down the search space of a fingerprint database. Experiments are conducted on different Fingerprint Verification Competition (FVC) databases and the results show that, while maintaining high hit rate the proposed method achieves lower penetration rate than existing methods.

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A Framework for Efficient Fingerprint Identification Using a Minutiae Tree

Cited by 28 publications

References 8 publications

Enrolment and Matching of Fingerprints using Minutiae Tree

Enrolment and Matching of Fingerprints using Minutiae Tree

Design & Implementation of Fingerprint Biometrics based on Discretized Fingerprint Texture Descriptor

Classification of Extended Delaunay Triangulation for Fingerprint Indexing

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