NDLI: Automatic Fingerprint Identification Using Gray Hopfield Neural Network Improved by Run-Length Encoding

Content Provider	IEEE Xplore Digital Library
Author	Mutter, K.N. Jafri, Z.M. Aziz, A.A.
Copyright Year	2008
Description	Author affiliation: Sch. of Phys., Univ. Sains Malaysia, Gelugor (Mutter, K.N.; Jafri, Z.M.; Aziz, A.A.)
Abstract	This paper presents a new technique of fingerprint identification using gray Hopfield neural network (GHNN) improved by run-length encoding (RLE). Gabor filter has been used for image enhancement at the stage of enrollment and vector field algorithm for core detection as a reference point. Finding this point will enable to cover most of information around the core. GHNN deals with gray level images by learning on bitplanes that represent the fingerprint image layers. For large number of images GHNN's memory needs very large storage space to cover all learned fingerprint images. RLE is a very simple and useful solution for saving the capacity of the net memory by encoding the stored weights, in which the weights data will reduce according to the repeated one. Experiments carried out on fingerprint images show that the proposed technique is useful in a number of different samples of fingerprint images in terms of converged images in quality, encoding and decoding performance.
Starting Page	205
Ending Page	210
File Size	1380968
Page Count	6
File Format	PDF
ISBN	9780769533599
DOI	10.1109/CGIV.2008.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-26
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Run-Length Encoding Automatic Fingerprint Identification Image matching Neurons Artificial neural networks Gray Hopfield Neural Network Fingerprint recognition Encoding Gabor filters
Content Type	Text
Resource Type	Article

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