A non-rigid registration method with application to distorted fingerprint matching

Lan, Sheng; Guo, Zhenhua; You, Jane

doi:10.1016/j.patcog.2019.05.021

Cited by 23 publications

(6 citation statements)

References 19 publications

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“…Another nonrigid registration method is proposed by Lan et al [22]. The novel approach combines the ridges' traditional minutiae and direction information, while the ridges' direction is frequently neglected or used indirectly.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, these algorithms should find optimal parameters with maximum ridge matching [15][16][17][18]. Multiple feature-extracting algorithms have been proposed to correct distorted fingerprints [13], [19], [20], [21], [22], [23]. These new algorithms are much more accurate than traditional minutiaebased or ridge-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Some literature proved validity in the public test databases, including FVC2004 DB1, FVC2006 DB2, Tsinghua DF, NIST SD14, and NIST SD27 [13], [19][20][21][22][23][24][25][26][27], [29]. Application scenarios are limited in practical work due to two primary reasons.…”

Section: Introductionmentioning

confidence: 99%

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A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

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With the widely used fingerprints to identify criminals, the influence of fingerprint deformation arouses attention in forensic science. To date, many approaches have been proposed to rectify distorted fingerprints. However, the performance in handling low-quality fingerprints extracted at a crime scene is less high quality than expected. This paper presents a combined method to rectify the latent fingerprints extracted at a crime scene. The method is a coarse-to-fine approach, combining the robustness of traditional pattern recognition and the accuracy of deep learning networks. We conducted several experiments to compare our approach with other approaches, including the nearest-neighbor search and network methods. The results show a remarkable improvement in fingerprint matching, especially in lowquality latent fingerprints. The top 25 cumulative match rate improves from 0.65 (original) to 1 (proposed method), whereas other approaches improve the result to 0.85 at best.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

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“…Today, point set/cloud registration is a noteworthy problem since it contributes to various fields such as computer vision, pattern recognition, image processing and classification (see in [1][2][3][4]), medical imaging and diagnosis (see in [5]), 3D modeling and construction (see in [6][7][8]), machine learning (see in [9]) and other engineering fields. The point cloud registration problem can be defined as finding the spatial transformation between two point sets by aligning them in a space.…”

Section: Introductionmentioning

confidence: 99%

Robust affine registration method using line/surface normals and correntropy criterion

Yïlmaz

Temeltaş

2022

Complex Intell. Syst.

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The problem of matching point clouds is an efficient way of registration, which is significant for many research fields including computer vision, machine learning, and robotics. There may be linear or non-linear transformation between point clouds, but determining the affine relation is more challenging among linear cases. Various methods have been presented to overcome this problem in the literature and one of them is the affine variant of the iterative closest point (ICP) algorithm. However, traditional affine ICP variants are highly sensitive to effects such as noises, deformations, and outliers; the least-square metric is substituted with the correntropy criterion to increase the robustness of ICPs to such effects. Correntropy-based robust affine ICPs available in the literature use point-to-point metric to estimate transformation between point clouds. Conversely, in this study, a line/surface normal that examines point-to-curve or point-to-plane distances is employed together with the correntropy criterion for affine point cloud registration problems. First, the maximum correntropy criterion measure is built for line/surface normal conditions. Then, the closed-form solution that maximizes the similarity between point sets is achieved for 2D registration and extended for 3D registration. Finally, the application procedure of the developed robust affine ICP method is given and its registration performance is examined through extensive experiments on 2D and 3D point sets. The results achieved highlight that our method can align point clouds more robustly and precisely than the state-of-the-art methods in the literature, while the registration time of the process remains at reasonable levels.

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“…Nonrigid point set registration is broadly applied in computer vision fields, such as face recognition, fingerprint matching, object tracking, remote sensing, medical image processing, and simultaneous localization and mapping (SLAM) [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences

Feng

2020

Sensors

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The nonrigid point set registration is one of the bottlenecks and has the wide applications in computer vision, pattern recognition, image fusion, video processing, and so on. In a nonrigid point set registration problem, finding the point-to-point correspondences is challengeable because of the various image degradations. In this paper, a robust method is proposed to accurately determine the correspondences by fusing the two complementary structural features, including the spatial location of a point and the local structure around it. The former is used to define the absolute distance (AD), and the latter is exploited to define the relative distance (RD). The AD-correspondences and the RD-correspondences can be established based on AD and RD, respectively. The neighboring corresponding consistency is employed to assign the confidence for each RD-correspondence. The proposed heuristic method combines the AD-correspondences and the RD-correspondences to determine the corresponding relationship between two point sets, which can significantly improve the corresponding accuracy. Subsequently, the thin plate spline (TPS) is employed as the transformation function. At each step, the closed-form solutions of the affine and nonaffine parts of TPS can be independently and robustly solved. It facilitates to analyze and control the registration process. Experimental results demonstrate that our method can achieve better performance than several existing state-of-the-art methods.

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A non-rigid registration method with application to distorted fingerprint matching

Cited by 23 publications

References 19 publications

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Robust affine registration method using line/surface normals and correntropy criterion

A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences

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