Enhancing Fingerprint Image through Ridge Orientation with Neural Network Approach and Ternarization for Effective Minutiae Extraction

Among the various biometric identifications available, fingerprints authentication is the oldest and the most reliable source of authentication. Identification of fingerprints basically relies on minutiae extraction. In most cases the finger print images that are obtained are of poor quality due to...

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Bibliographic Details
Published in:International journal of machine learning and computing 2012-08, Vol.2 (4), p.397-401
Main Authors: Stephen, M James, v G D, P, Reddy, Prasad
Format: Article
Language:English
Subjects:
Blocking
Extraction
Fingerprints
Fingers
Image enhancement
Neural networks
Ridges
Citations: Items that this one cites
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Summary:Among the various biometric identifications available, fingerprints authentication is the oldest and the most reliable source of authentication. Identification of fingerprints basically relies on minutiae extraction. In most cases the finger print images that are obtained are of poor quality due to various reasons such as scars, dirt, non-uniform ink intensity and skin diversities. So enhancement of image prior to extraction increases the consistency. In this paper a new methodology was proposed for finger print image enhancement where the process begins with ridge orientation using neural network approach that follows dividing the image into white block, black block and grey block, which is called ternarisation, which is a new methodology to deal with uncertainty of minutiae. Then the image is binarised using the concept of pixel aggregation. And finally thinning is applied before proceeding to effective minutiae extraction.
ISSN:2010-3700
2010-3700
DOI:10.7763/IJMLC.2012.V2.152