Fingerprint Indexing Based on Combination of Novel Minutiae Triplet Features

Zhou, Wei; Hu, Jiankun; Wang, Song; Petersen, Ian R.; Bennamoun, Mohammed

doi:10.1007/978-3-319-11698-3_29

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…Liang et al [9] also proposed low-order Delaunay triangle representation of minutiae. This approach is also similar to the one presented in [1] and [12] except that theirs is based on a well supported geometric theory. In addition to low-order Delaunay triangles they also validated minutiae triplets by creating circles around each detected triangle, see Figure 6.…”

Section: Related Workmentioning

confidence: 95%

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A Fingerprint Indexing Approach Using Multiple Similarity Measures and Spectral Clustering

Mngenge

Mthembu

Nelwamondo

et al. 2015

2015 12th Conference on Computer and Robot Vision

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Fingerprint identification is still an open problem. Many proposed solutions relies on expert's predefined classes.Recent studies and experiments have shown that predefined classes are not suitable for large databases. The main challenge is the distribution of predefined expert's classes. Most fingerprints are loops, this means an identification system designed based on this classical approach slows down a matching process due to many 1 − 1 comparisons that needs to be done to find an exact match. Beside this, some fingerprints sometimes lose their core points. In such case, it becomes very difficult to find the right class. To solve this problem, minutiae points positions (x, y) and orientations (θ) are proposed in this paper. Minutiae points are extracted and passed to a feature dimensions reduction algorithm composed of locality sensitive hashing (LSH) and histograms. The algorithm proposed creates 260 fixed length feature vectors for each fingerprint. Feature vectors are then passed to a spectral clustering which automatically creates 25 classes. Classes are then indexed through three similarity measures, namely, Euclidean, Cosine and Minkowski distance. The performance of the proposed approach is compared with two fingerprint identification systems which are based on Minutiae Cylinder-Code (MCC) and Minutiae Quadruplets (MQ). The proposed approach is 89.6% accurate while MCC and MQ approaches gets 89.4% and 79.56%, respectively, on a NIST 4 special database.

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Section: Related Workmentioning

confidence: 95%

“…This affects even triangular features which were not formed based on that missing minutiae point. Zhou et al [12] also proposed a similar triangular approach formed by minutiae triplets. Their approach has the same drawbacks as the one proposed in [1].…”

Section: Related Workmentioning

confidence: 99%

A Fingerprint Indexing Approach Using Multiple Similarity Measures and Spectral Clustering

Mngenge

Mthembu

Nelwamondo

et al. 2015

2015 12th Conference on Computer and Robot Vision

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“…Fingerprint indexing is applied to reduce the search space and thus to reduce the search time in a large database. Lot of fingerprint indexing techniques have been proposed and applied by researchers to reduce the response time for improving the effectiveness of the identification system (Li et al, 2014;Iloanusi et al, 2011;Zhou et al, 2014;Mngenge et al, 2015). Another indexing method known as Minutiae Cylinder Code (MCC) uses a 3D data structure known as cylinder to speed up the fingerprint identification (Cappelli et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

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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“…Therefore, restrictions seem reasonable. Kovács-Vajna [23], Bhanu and Tan [8], Reddy et al [37], and Zhou et al [45] proposed variations in the selection of relevant triplets and features to be extracted. These strategies allow keeping the number of triplets to a reasonable order.…”

Section: Approaches Using Fp Minutiaementioning

confidence: 99%

“…[37], and Zhou et al . [45] proposed variations in the selection of relevant triplets and features to be extracted. These strategies allow keeping the number of triplets to a reasonable order.…”

Section: Relevant Approachesmentioning

confidence: 99%

Survey on features for fingerprint indexing

Schuch¹

2018

IET biom.

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Nowadays, several biometric databases already contain millions of entries of individuals. With an increasing number of enrolled individuals, the response time of queries grows and can become critical. Fingerprint indexing offers a set of techniques to reduce the workload of entries, which have to be compared thoroughly. This work surveys research on such techniques. It focuses on the features of fingerprints, which are used as input. This survey also provides an assessment of the quality of the body of research in this field. Deficiencies herein are identified, e.g. there is a lack of common datasets and metrics used for testing.

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Fingerprint Indexing Based on Combination of Novel Minutiae Triplet Features

Cited by 16 publications

References 27 publications

A Fingerprint Indexing Approach Using Multiple Similarity Measures and Spectral Clustering

A Fingerprint Indexing Approach Using Multiple Similarity Measures and Spectral Clustering

Enrolment and Matching of Fingerprints using Minutiae Tree

Survey on features for fingerprint indexing

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