A Robust Fingerprint Indexing Scheme Using Minutia Neighborhood Structure and Low-Order Delaunay Triangles

Liang, Xuefeng; Bishnu, Arijit; Asano, Tomohiko

doi:10.1109/tifs.2007.910242

Cited by 93 publications

(44 citation statements)

References 13 publications

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“…There have been many fingerprint indexing approaches that have been proposed before using minutiae [10][11][12][13][14][15], local ridge orientation [7], singular points [6], SIFT features [9] and matching scores [8]. The ones based on minutiae have proved to be the most robust and we discuss some of the state-of-the-art techniques of fingerprint indexing below.…”

Section: Related Workmentioning

confidence: 99%

“…The features used were the ratio of the largest side with the smallest sides and the cosine angle between the smallest sides. Low-order Delaunay Triangulation [13] reduced the effect of spurious and missing minutiae thereby further increasing the performance. The authors in this paper included features such as triangle handedness, minimum and median angles and length of the longest side in the triangle.…”

Section: Related Workmentioning

confidence: 99%

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A novel fingerprint indexing scheme using dynamic clustering

Jain

Prasad

2016

J Reliable Intell Environ

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With the increase in adoption of biometric data for various identification processes, the need for efficient indexing techniques is also on the rise as it is computationally impossible to match the query template against every enrolled entry in the database. In this paper, we have proposed two robust feature extraction techniques from fingerprint images followed by an indexing method based on clustering and m-ary trees. We extract robust translation and rotation invariant features from O(n) triangles obtained from the triangle spiral constructed for every fingerprint. The other method involves construction of M rectangles around every reference minutiae and calculation of features based on neighboring relation. Experiments with the benchmark databases confirm the superiority of our approach to the other existing techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A novel fingerprint indexing scheme using dynamic clustering

Jain

Prasad

2016

J Reliable Intell Environ

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“…The singular points (SPs) are the discontinuities in the directional field [5]. In [7] X. Liang et al concentrate on a more accurate fingerprint indexing method that retrieves the top N possible matching candidate list from a large biometric database. Based on minutia neighborhood and a more secure triangulation algorithm (low-order Delaunay triangles, consisting of order 0 and 1 Delaunay triangles),are used which are both insensitive to fingerprint distortion.…”

Section: Fingerprint Indexing Based On Local Featuresmentioning

confidence: 99%

Fingerprint Indexing Approaches for Biometric Database: A Review

Parmar¹,

Degadwala²

2015

IJCA

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Due to the uniqueness and persistent properties of the biometric fingerprint characteristics, large scale in border control and governmental applications such as the Aadhaar project in India, the Visa Information System (VIS) in Europe and US-VISIT / IDENT system in the USA are based on fingerprint recognition and generally contain millions of fingerprint samples. The study of fingerprint indexing techniques is inevitable, to improve the effectiveness in searching for satisfactory candidate reference list in such large biometric databases. In systems using biometric identification, the identity associated with the input data is decided by its comparison against every single entry in the database. This matching process is exhaustive which leads to increase in the rate of erroneous identification and the response time of the system. This paper presents a survey on the fingerprint indexing methods that are currently available and some of them are presented. Fingerprint indexing is based on the local ridge line orientation, global feature, minutiae and other features.

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“…For the purpose of identification, we compute the histogram intersection to better handle partial overlap [46]. This approach is related to "geometric hashing" [47]: a hash table is built by quantizing geometric objects like minutiae triplets which were used in [48,49,50].…”

Section: Identificationmentioning

confidence: 99%

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

Gottschlich

Huckemann

2014

IET biom.

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In this study 1 we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a non-parametric distribution based method using second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This 'test of realness' can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.

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A Robust Fingerprint Indexing Scheme Using Minutia Neighborhood Structure and Low-Order Delaunay Triangles

Cited by 93 publications

References 13 publications

A novel fingerprint indexing scheme using dynamic clustering

A novel fingerprint indexing scheme using dynamic clustering

Fingerprint Indexing Approaches for Biometric Database: A Review

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

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