Fingerprint classification using an AM-FM model

Pattichis, Marios S.; Panayi, G.C.; Bovik, Alan C.; Hsu, Shun‐Pin

doi:10.1109/83.923291

Cited by 41 publications

(23 citation statements)

References 13 publications

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“…Pattichis et al in [17] focus their work primarily on the image and feature enhancement and on finding improved classifiers, and less on the development of novel fingerprint representations. Using an AM-FM (Amplitude Modulated-Frequency Modulated) representation for each fingerprint, they obtain significant gains in classification performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier

Vitello

Sorbello

Migliore

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images; the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancement phase. Experimental trials, conducted on a subset of the free downloadable PolyU database, show a classification rate of 91% over a 100 images test database using only 12 training examples.

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Section: Related Workmentioning

confidence: 99%

A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier

Vitello

Sorbello

Migliore

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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“…Recent applications of the AM--FM model range from image interpolation, fingerprint classification, image segmentation, and video segmentation [15][16][17]. In general, an image is modeled by an AM--FM expansion such that:…”

Section: Am-fm Analysismentioning

confidence: 99%

AM–FM techniques in the analysis of optical coherence tomography signals

Pitris

Kartakoullis

Bousi

2009

Journal of Biophotonics

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The subtle tissue changes associated with the early stages of malignancies, such as cancer, are not clearly discernible even at the current, improved, resolution of optical coherence tomography (OCT) systems. However, these changes directly affect the spectral content of the OCT image that contains information regarding these unresolvable features. Spectral analysis of OCT signals has recently been shown to provide additional information, resulting in improved contrast, directly related to scatterer size changes. Amplitude modulation-frequency modulation (AM-FM) analysis, a fast and accurate technique for the estimation of the instantaneous frequency, phase, and amplitude of a signal, can also be applied to OCT images to extract scatterer-size information. The proposed technique could make available an extremely valuable tool for the investigation of disease characteristics that now remain below the resolution of OCT and could significantly improve the technology's diagnostic capabilities.

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“…AM-FM models have been used in many image pattern analysis applications, including shape from shading [31], image interpolation [32], fingerprint classification [33], image retrieval in digital libraries [34], image segmentation [35], [36], [37], and video segmentation [38].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Image Structure by Multidimensional Frequency Modulation

Pattichis

Bovik

2007

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We develop a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images. We begin with the widely accepted definition of the instantaneous frequency vector (IF) as the gradient of the phase and define the instantaneous frequency gradient tensor (IFGT) as the tensor of component derivatives of the IF vector. Frequency modulation bounds are derived and interpreted in terms of the eigendecomposition of the IFGT. Using the IFGT, we derive the ordinary differential equations (ODEs) that describe image flowlines. We study the diagonalization of the ODEs of multidimensional frequency modulation on the IFGT eigenvector coordinate system and suggest that separable transforms can be computed along these coordinates. We illustrate these new methods of image pattern analysis on textured and fingerprint images. We envision that this work will find value in applications involving the analysis of image textures that are nonstationary yet exhibit local regularity. Examples of such textures abound in nature.Index Terms-Theory and models, image processing and computer vision, image models.

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Fingerprint classification using an AM-FM model

Cited by 41 publications

References 13 publications

A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier

A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier

AM–FM techniques in the analysis of optical coherence tomography signals

Analyzing Image Structure by Multidimensional Frequency Modulation

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