The Research And Applying Of Pulse Coupled Neural Network For Fingerprint Images Processing

PCNN (Pulse Coupled Neural Network) is a new kind of Artificial Neural Networks (ANN) based on the phenomena of synchronous pulse bursts in the animal visual cortex. PCNN is different from those models of traditional artificial neural network. It is constructed by simulating the activities of the neurons of visual cortex, and is the simplification and approximation of the neurons. The properties inherited in PCNN, such as linking field and the dynamic threshold, make the approximate neurons fire simultaneously, and this is very close to the natures of visual cortex of small mammals. So it has gained widely applications in image processing, such as image segmentation, edge extraction, object recognition and so on.This thesis investigates the model of PCNN, which includes its basic principles, behaviors and characteristics. In this thesis, through reducing some parameters we get a simplified PCNN which also has the linking field and the dynamic threshold, and simplified PCNN is adapt to image processing. Theoretically speaking, simplified PCNN can be used in every aspect of image processing. This thesis investigates the segmentation of fingerprint image, and the smoothing of the fingerprint image, which is polluted by Gaussian white noise. The segmentation and smoothing are two important parts of fingerprint identification system. Simplified PCNN is also effective when dealing with other kinds of image.The results of experiment show that this method mentioned in this article is superior to other algorithm when it is used in fingerprint image segmentation and smoothing. And this method has high accuracy to satisfy the demand of fingerprint identification system. Simplified PCNN has a fine adaptability of brightness and contrast. There is no difference between the images with different brightness and contrast.