Fast focal point localization algorithm for fingerprint registration

Areekul, Vutipong; Boonchaiseree, Natthawat

doi:10.1109/iciea.2008.4582888

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…We referred to the methods used in fingerprint pattern classification field which is mainly based on smoothing processing. The refining method achieved a good result in 2D case [24], [30], [31]. We generalize the method to 3D manifold.…”

Section: Noise Deductionmentioning

confidence: 89%

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

et al. 2019

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Iterative closest point algorithms suffer from non-convergence and local minima when dealing with cloud points with a different sampling density. Alternative global or semi-global registration algorithms may suffer from efficiency problem. This paper proposes a new registration algorithm through the differential topological singularity points (DTSP) based on the Helmholtz-Hodge decomposition (HHD), which is called DTSP-ICP method. The DTSP-ICP method contains two algorithms. First, the curvature gradient fields on surfaces are decomposed by the HHD into three orthogonal parts: divergence-free vector field, curl-free vector field, and a harmonic vector field, and then the DTSP algorithm is used to extract the differential topological singularity points in the curl-free vector field. Second, the ICP algorithm is utilized to register the singularity points into one aligned model. The singularity points represent the feature of the whole model, and the DTSP algorithm is designed to capture the nature of the differential topological structure of a mesh model. Through the singularity alignment, the DTSP-ICP method, therefore, possesses better performance in triangular model registration. The experimental results show that independent of sampling schemes, the proposed DTSP-ICP method can maintain convergence and robustness in cases where other alignment algorithms including the ICP alone are unstable. Moreover, this DTSP-ICP method can avoid the local errors of model registration based on Euclidean distance and overcome the computation insufficiencies observed in other global or semi-global registration publications. Finally, we demonstrate the significance of the DTSP-ICP algorithm's advantages on a variety of challenging models through result comparison with that of two other typical methods. INDEX TERMSIterative closest point algorithm, computer aided manufacturing, differential equations, HHD.

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Section: Noise Deductionmentioning

confidence: 89%

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

et al. 2019

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“…Then linear projection can be applied to relocate an obtained focal point from one image to another image. A location of the focal point is projected onto two sides of a triangle, formed by 3 manually marked points [12]. By keeping the same projection ratio of triangle sides, the projected focal point can be relocated from one image to another image, and distance error can be measured.…”

Section: A) Evaluation Methods A: Approximately Linear Projection Methmentioning

confidence: 99%

“…However, an expected area can be estimated. Therefore, in [12], the method "A" is introduced by linearly projecting an obtained focal point into another fingerprint image with 3 manually reference points. The second method "B" manually projects all obtained focal points into one fingerprint image.…”

Section: Distance Error Measurementmentioning

confidence: 99%

“…However, this algorithm still needs fingerprint enhancement in order to effectively track local ridges. Recently Areekul and Boonchaiseree [12] introduced the third algorithm by analyzing a focal point from orientation fields instead of true ridges in order to reduce computational complexity without losing accuracy. In addition, this previous algorithm introduced a fixed half circle window which is suitably designed for orientation field selection, resulted in algorithm having a good performance.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the concept of a half concentric lens or a half circle window from [12] is extended with directional adaptation. Moreover, concentric lens similarity factor is introduced in order to control algorithm's convergence.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Focal Point Detection Based on Half Concentric Lens Model for Singular Point Extraction in Fingerprint

Boonchaiseree

Areekul

2009

Advances in Biometrics

Self Cite

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Abstract.A focal point is a kind of singular points, closely related to a core point, which can be derived from curvature of fingerprint ridges and valleys. It is expected that the focal point is more reliable than the core point in case of low quality fingerprint. This paper proposes a new efficient focal point localization method based on a half concentric lens model. The half concentric lens window, with directional adaptive, accelerates convergence of a focal point localization process rapidly. Moreover, concentric lens similarity factor is introduced in order to measure orientation and stability of an extracted focal point. From experimental results, the proposed scheme is out-performed most of singular point detection schemes in literature in term of location accuracy and consistency. For computational complexity, algorithm requires average 75 millisecond execution-time from original fingerprint to a unique focal point.

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Fingerprint Identification — Ideas, Influences, and Trends of New Age

Bharkad¹,

Kokare²

2011

Pattern Recognition, Machine Intelligence and Biometrics

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Fast focal point localization algorithm for fingerprint registration

Cited by 5 publications

References 10 publications

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

Focal Point Detection Based on Half Concentric Lens Model for Singular Point Extraction in Fingerprint

Fingerprint Identification — Ideas, Influences, and Trends of New Age

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