Fingerprint matching using OrientationCodes and PolyLines

Wang, Xuchu; Li, Jianwei; Niu, Yanmin

doi:10.1016/j.patcog.2007.02.020

Cited by 33 publications

(15 citation statements)

References 22 publications

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“…2π). From this cylinder, it is possible to find its neighbouring minutiae of which the information used to form the characteristics of this minutia is the position (Chikkerur, Cartwright, & Govindaraju, 2006;Medina-Pérez, García-Borroto, Gutierrez-Rodriguez, & Altamirano-Robles, 2011), ridge (Feng, Ouyang, & Cai, 2006;Wang, Li, & Niu, 2007), orientation (Qi, Yang, & Wang, 2005;Tico & Kuosmanen, 2003), or a combination of these (Feng, 2008).…”

Section: Minutia Cylinder-codementioning

confidence: 99%

Speeding up and enhancing a large-scale fingerprint identification system on GPU

Nguyen

2017

Journal of Information and Telecommunication

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Fingerprint identification is one of the most common biometric feature problems which is used in many applications. Although state-of-the-art algorithms are very accurate, the need for fast processing a big database of millions of fingerprints is highly demanding. In this paper, we propose to adapt the fingerprint matching algorithm based on Minutia Cylinder-Code on Graphics Processing Units to speed up the matching. Another contribution of this paper is to add a consolidation stage after matching to enhance the precision. The experimental results on a GTX-680 and K40 tesla devices with standard data-sets prove that the proposed algorithm can be comparable with the state-of-the-art approach, and is suitable for a real-time identification application. ARTICLE HISTORY

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Section: Minutia Cylinder-codementioning

confidence: 99%

Speeding up and enhancing a large-scale fingerprint identification system on GPU

Nguyen

2017

Journal of Information and Telecommunication

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“…For each query minutia and for each template minutia , compute the possibility value using equation (4 …”

Section: Letmentioning

confidence: 99%

“…A way to deal with this problem is enriching the minutia representation with additional information known as minutia descriptors. Minutia descriptors can be mainly classified in: ridge based descriptors [4][5][6][7][8], orientation based descriptors [4,[9][10][11] and local neighboring minutiae based descriptors [3,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor

Medina‐Pérez

Gutierrez-Rodríguez

García-Borroto

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. This paper reviews a well-known fingerprint matching algorithm that uses an orientation-based minutia descriptor. It introduces a set of improvements to the algorithm that increase the accuracy and speed, using the same features. The most significant improvement is in the global minutiae matching step, reducing the number of local matching minutiae and using multiple minutiae pairs for fingerprint alignment. We conduct a series of experiments over the four databases of FVC2004, showing that the modified algorithm outperforms its predecessor and other algorithms proposed in the literature.

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“…This method obtained a good trade-off between matching performance and computational expense. Wang et al (2007) proposed a feature called PolyLines to extract ridge information. Three transformation-invariant features were calculated for each ridge sampling point and ridge similarity was based on these features.…”

Section: Introductionmentioning

confidence: 99%

“…The difference between them is the sampling strategy. Wang et al (2007) proposed a feature called OrientationCodes, in which the ROI was circularly tessellated through several bands and sectors, and the orientation was estimated by least square error. Gu et al (2006) and Qi et al (2005) viewed the orientation field as a global feature and combined it with minutiae to match fingerprints.…”

Section: Introductionmentioning

confidence: 99%