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2017 | OriginalPaper | Chapter

Fingerprint Image Segmentation Using Textural Features

Authors : Reji C. Joy, M. Azath

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

Automatic Fingerprint Identification System (AFIS) uses fingerprint segmentation as its pre-processing step. A fingerprint segmentation step divides the fingerprint image into foreground and background. An AFIS that uses a feature extraction algorithm for person identification will tend to fail if it extracts spurious features from the noisy background area. So fingerprint image segmentation plays a crucial role in reliably separating ridge like part (foreground) from its background. In this paper, an algorithm for fingerprint image segmentation using GLCM textural feature is presented. Four block level GLCM features: Contrast, Correlation, Energy and Homogeneity are used for fingerprint segmentation. A linear classifier is trained for classifying per block of fingerprint image. The algorithm is tested on standard FVC2002 dataset. Experimental results show that the proposed segmentation method works well in noisy fingerprint images.

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next chapter Improved Feature Selection for Neighbor Embedding Super-Resolution Using Zernike Moments
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Metadata
Title
Fingerprint Image Segmentation Using Textural Features
Authors
Reji C. Joy
M. Azath
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2106-0

Electronic ISBN: 978-981-10-2107-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2107-7_1