Possibilistic fuzzy c-means algorithm for fingerprint image

Raghotham, Reddy.G; Yugander, P.; Sheshagiri, Babu.J; Rameshwar, Rao.R

doi:10.1109/icdcsyst.2012.6188738

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…This method is evaluated in a test image in original form and also corrupted by gaussian and salt and pepper noise. Another method based in an adaptation of fuzzy c-means is proposed by Raghotham et al [Ra12]. This method combines the probabilistic fuzzy cmeans clustering (PFCM) algorithm (which is a mix of probabilistic c-means clustering (PCM) and fuzzy c-means clustering (FCM) algorithm allowing to overcome the noise sensitivity defect in FCM and coincident cluster problem in PCM) with an adaptative level set method.…”

Section: Related Workmentioning

confidence: 99%

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

Ferreira

Sequeira²,

Cardoso

et al. 2018

2018 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option-a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.

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Section: Related Workmentioning

confidence: 99%

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

Ferreira

Sequeira²,

Cardoso

et al. 2018

2018 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Fingerprint Identification based on Frequency Domain Analysis

Khazaal

Mahdi

2019

2019 4th Scientific International Conference Najaf (SICN)

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An Edge based Clustering Technique with Self-Organizing Maps

Chamundeswari¹,

Varma²,

Satyanarayana³

2018

IJITCS

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Recently, artificial neural networks are fund to be efficiently used in clustering algorithms. So, the present paper focuses on the development of a novel clustering method based on artificial neural networks. The present paper uses an enhancement filter to enhance the segments in the input image. After this, the various sub images are generated and features are computed for each sub and edge image. Finally, the Self Organizing Map (SOM) is used for clustering process. The proposed novel method is evaluated with a database of 795 leaf images. Further various Probability Distributed Functions (PDFs) are used to evaluate the efficacy of the proposed method. The performance measures of the proposed method indicate the efficiency of the extended clustering method with SOM.

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Possibilistic fuzzy c-means algorithm for fingerprint image

Cited by 3 publications

References 20 publications

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

Fingerprint Identification based on Frequency Domain Analysis

An Edge based Clustering Technique with Self-Organizing Maps

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