PCASYS - a pattern-level classification automation system for
                        fingerprints

Candela, Gerald T.; Grother, Patrick J.; Watson, Craig I.; Wilkinson, Ross; Wilson, Charles L.

doi:10.6028/nist.ir.5647

Cited by 142 publications

(117 citation statements)

References 3 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…In order to verify that a nonlinear discriminant analysis works efficiently in fingerprint classification, we utilized a NIST fingerprint image software PCASYS [6]. images were constructed by using Å in (7), and then KDA/GSVD was applied to the constructed directional images.…”

Section: Resultsmentioning

confidence: 99%

“…The local dominant directions are detected as Ñ Ü given in (4). The obtained dominant direction is represented as a vector Ó×´¾ µ × Ò´¾ µ℄ where ¼ ½ ¼ to avoid the discontinuity between ¼ and ½ ¼ as in [6]. Let …”

Section: Computation Of Directional Vectorsmentioning

confidence: 99%

“…The features generated after the preprocessing steps are fed into classifiers such as neural networks [4], hidden Markov model [5], probabilistic neural networks [6], support vector machines [7]. Jain et al extracted features from Gabor-filtered images which were centered on a core point and used two-stage classifiers of K-nearest neighbor and neural networks [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Park

2005

Pattern Recognition

View full text Add to dashboard Cite

In this paper, we present a new approach for fingerprint classification based on Discrete Fourier Transform (DFT) and nonlinear discriminant analysis. Utilizing the Discrete Fourier Transform and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Computation Of Directional Vectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Park

2005

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…By appropriately labeling the nodes and arcs of the graph, the authors obtained a structure which summarizes the topological features of the fingerprint and is invariant with respect to displacement and rotation. The PCASYS approach (Pattern-level Classification Automation SYStem) proposed by (Candela & Chellappa, 1993) and (Candela et al, 1995) assigns fingerprints to six nonoverlapping classes. Before computing the directional images, the ridge-line area is separated from the background and an enhancement is performed in the frequency domain.…”

Section: Structural Approachmentioning

confidence: 99%

Minutiae-based Fingerprint Extraction and Recognition

Zaeri¹

2011

Biometrics

View full text Add to dashboard Cite

“…However, these systems are subject to failure when his characters do not show or cannot be extracted. Wang [6] has presented one of these ways to take advantage of the orientation information. Chong [7] for the same purpose ridges represented by splined curves were employed.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Feature Extraction Using Convolution and Particle Swarm Optimization Algorithms

Alazzawi¹,

Al-Khalid²,

Alhasnawi³

2017

djps

View full text Add to dashboard Cite

Most of the existing fingerprint extraction systems are based on the global features and detailed characteristics of fingerprints, which have a weak performance in cases of poor quality fingerprint images, such as the fingerprint image is incomplete. In order to improve recognition accuracy, reliability and quickness to identify the fingerprints a new trend has been opened by using swarm intelligence techniques in biometric field. Therefore, particle swarm optimization techniques (PSO) are used in this paper to build fingerprints authentication system. A fast fingerprint identification method based on the convolution transformation and Particle Swarm Optimization algorithms proposed. The convolution algorithm was used to extract the convolved feature and then found the optimal solution from this feature by using Particle Swarm Optimization algorithm. Experimental results show that, the proposed method has a high efficiency in extracting features from fingerprints, strong strength, and good accuracy for recognition. Keywords

show abstract

PCASYS - a pattern-level classification automation system for fingerprints

Abstract: This report describes a system we have developed

Cited by 142 publications

References 3 publications

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Minutiae-based Fingerprint Extraction and Recognition

Fingerprint Feature Extraction Using Convolution and Particle Swarm Optimization Algorithms

Contact Info

Product

Resources

About