A Robust Point Sets Matching Method

Liu, Xiao; Han, Congying; Guo, Tong

doi:10.1007/978-3-319-20469-7_41

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…In this paper, an affine transformation is used. Other geometric transformations used in fingerprint matching are rigid transformation [9], non-linear and topological transformations.…”

Section: Point-set Based Fingerprint Matchingmentioning

confidence: 99%

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Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Udo

Akhigbe

Afolabi

2019

AJRCoS

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Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy. Study Design: Statistical and theoretical research approaches. Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018. Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab. Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions. Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

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“…In this paper, an affine transformation is used. Other geometric transformations used in fingerprint matching are rigid transformation [9], non-linear and topological transformations.…”

Section: Point-set Based Fingerprint Matchingmentioning

confidence: 99%

“…DB3 in FVC2004) performed poorly with this approach. Liu et al, [9] studied a robust point set matching method to find optimum or suboptimal spatial mapping between the two point sets. However, the study assumed the point sets in two-dimensional space as directed and considered only the location feature of point only.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Udo

Akhigbe

Afolabi

2019

AJRCoS

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“…We use the same method as in [2], [26] to generate the point overlapping dataset. We generate twenty-five point-sets with 70 points in each set.…”

Section: A Experiments On Point-set Matching With Limitationsmentioning

confidence: 99%

“…Many efforts have been made to find the affine invariant relationship between the elements of point-set for feature extraction. Liu et al described that the point-set matching is always matching two point-sets with noise and outliers [2]. Most state-of-the-art image matching methods are based on frame and texture, which utilise sequential attributes as features.…”

Section: Introductionmentioning

confidence: 99%

“…Ho and Yang [5] treated points as complex numbers and interpreted that the corresponding objects were mutually transformed with a coefficient, which was computed from unit complex numbers without knowing the correspondence. Liu et al [2] treated rigid point-set matching as the similarity of transformations on x-axis and y-axis, and took this similarity between point and its neighbours to calculate the matching similarity of point-sets. A rich descriptor was introduced by Grigorescu and Petkov [6] to describe spatial relationship between feature points.…”

Section: Introductionmentioning

confidence: 99%

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Affine Invariant Point-Set Matching Using Convex Hull Bisection

Wang

Gao

et al. 2016

2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Affine invariant point-set matching is an important issue in computer vision and pattern recognition. Using reference points derived from the convex hull of the point-set is an existing idea to solve this problem. However, how to choose proper and enough reference points for extracting affine invariant and powerful discriminative descriptors is an open problem. In this paper, a novel method termed convex hull bisection (CHB) is proposed for affine invariant point-set matching. In CHB, for each point in the point-set, two reference points are derived by bisecting the convex hull using the line connecting the point and the centroid of the convex hull. The resulting reference points and other reference points, such as the centroid point of the convex hull and the mean point of the point-set, are utilised to yield an 11-dimensional affine invariant feature vector associated with any point within the point-set. The obtained point based descriptors are used for point-set matching. The proposed method works well even in the case that reference points are overlapping or collinear, which overcomes the limitations of the existing convex hull based methods. The effectiveness of the proposed method is validated by an extensive experimental investigation.

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Feature point matching based on ABC-NCC algorithm

Banharnsakun

2017

Evolving Systems

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A Robust Point Sets Matching Method

Cited by 3 publications

References 10 publications

Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Affine Invariant Point-Set Matching Using Convex Hull Bisection

Feature point matching based on ABC-NCC algorithm

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