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

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.