&lt;title&gt;Personal verification system with high tolerance of poor-quality fingerprints&lt;/title&gt;

Sasakawa, Koichi; Isogai, Fumihiko; Ikebata, Shigeki

doi:10.1117/12.25401

Cited by 9 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noting this kind of the division of roles, the study of a two-stage verification method, in which positional deviation is compensated in the first stage on the basis of the global directional distribution of the fingerprint ridges, and minutiae matching is performed in the second stage, has been proposed [11]. The framework of two-stage verification is used in this study.…”

Section: Conventional Methods and Characterization Of Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fingerprint verification using ridge direction distribution and minutiae correspondence

Wakahara

Kimura

Suzuki

et al. 2007

Systems & Computers in Japan

View full text Add to dashboard Cite

SUMMARYFingerprint verification technique is raising high expectations as a means of personal identification based on physical features. This paper proposes a fingerprint verification method which can achieve minutiae matching with highly accurate compensation for rotation and position deviation, as well as high speed and stability. The process flow after binarization of the gray-scale fingerprint image is as follows. (1) Using the fingerprint ridge direction distribution in each local region, highly accurate compensation for the rotation and the position deviation between the enrolled fingerprint and the input fingerprint is performed. (2) The optimal correspondence between enrolled and input minutiae is established by fast combinatorial search. (3) By applying threshold processing to the distances of the fingerprint ridge direction distributions and the minutiae matching rate, the acceptance/rejection of the person is decided. Fingerprint image data for 80 persons × 4 fingers × 10 samples were collected using a commercial electrostatic capacitance semiconductor fingerprint sensor. The false reject rate and the false acceptance rate are investigated in fingerprint verification experiments, and the effectiveness of the proposed method is demonstrated.

show abstract

Section: Conventional Methods and Characterization Of Proposed Methodsmentioning

confidence: 99%

“…(7)] and the minutiae matching rate κ [Eq. (11)] obtained in Section 3.2. The acceptance is judged by the logical product of these conditions.…”

Section: Judgment Of Acceptance or Rejectionmentioning

confidence: 99%

Fingerprint verification using ridge direction distribution and minutiae correspondence

Wakahara

Kimura

Suzuki

et al. 2007

Systems & Computers in Japan

View full text Add to dashboard Cite

show abstract

“…The major difference is that the proposed approach separates the non-linear scale-selection and shape-adaptation procedure from the linear anisotropic diffusion scheme, thus bearing closer relationships to the open-ended suggestion in [21] 2 ∇ 2 µ. Algorithmic structure. The general structure of the proposed method resembles the classical structure presented in section I, and used by most classical works on fingerprint image enhancement [3], [8], [9], [10], [11], consisting of an estimation of local image parameters, followed by the application of contextual filters. In this respect, a major difference of the proposed algorithm consists of its iterative coarse to fine structure, where initial coarse estimates lead to a coarsely enhanced image, which is later used to produce more refined estimates and enhanced images, until convergence criteria are achieved.…”

Section: Relations To Previous Workmentioning

confidence: 99%

“…Traditional works on fingerprint enhancement [3], [8], [9], [10], [11] performed local estimates as well as contextual filtering in a highly discretized manner. These methods often resulted in blocky artifacts in the output as well as in poor estimates of the local image characteristics.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

Almansa

Lindeberg

2000

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives.The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.

show abstract

“…Moreover, biometric data is protected by using the cryptology, and there is a method of correcting swinging of the input image by using helper data [2]. Now we use fingerprint authentication scheme using features extracted from fingerprint images [3]. We propose a method of fingerprint matching based on matching scores with other data [4].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Authentication Based on Matching Scores with Other Data

Maeda

Matsushita

Sasakawa

et al. 2005

Advances in Biometrics

Self Cite

View full text Add to dashboard Cite

Abstract.A method of person authentication based on matching scores with the fingerprint data of others is proposed. Fingerprint data of others is prepared in advance as a set of representative data. Input fingerprint data is verified against the representative data, and the person belonging to the fingerprint is confirmed from the set of matching scores. The set of scores can be thought of as a feature vector, and is compared with the feature vector already enrolled. In this paper, the mechanism of the proposed method, the person authentication system using this method are described, and its advantage. Moreover, the simple criterion and selection method of the representative data are discussed. The basic performance when general techniques are used for the classifier is FNMR-3.6% at FMR-0.1%.

show abstract

<title>Personal verification system with high tolerance of poor-quality fingerprints</title>

Cited by 9 publications

References 5 publications

Fingerprint verification using ridge direction distribution and minutiae correspondence

Fingerprint verification using ridge direction distribution and minutiae correspondence

Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

Fingerprint Authentication Based on Matching Scores with Other Data

Contact Info

Product

Resources

About