This paper analyzes the Hopfield neural network for storage and recall of fingerprint images. The paper first discusses the storage and recall via hebbian learning rule and then the performance enhancement via the pseudo-inverse learning rule. Performance is measured with respect to storage capacity; recall of distorted or noisy patterns. Here we test the accretive behavior of the Hopfield neural network.

This paper focused on the study of various feature extraction techniques applied for fingerprint identification, verification and classification as it is the most important step for image processing. Feature extraction techniques are classified into local (low level) and global (high level) features. Global features such as arch, loop, delta and whorl where as local features such as ridge end and bifurcation called minutiae are majors of automatic fingerprint recognition system. In this study, it has been observed that most of the fingerprint recognition systems are based on minutiae features. In this paper we analyze the various feature extraction methods used so far with their mathematical background to the readers.

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Automatic character recognition is one of the challenging fields in pattern recognition especially for handwritten Odia characters as many of these characters are similar and rounded in shape. In this paper, a comparative performance analysis of Hopfield neural network for storing and recalling of handwritten and printed Odia characters with three different learning rules such as Hebbian, Pseudo-inverse and Storkey learning rule has been presented. An experimental exploration of these three learning rules in Hopfield network has been performed in two different ways to measure the performance of the network to corrupted patterns. In the first experimental work, an attempt has been proposed to demonstrate the performance of storing and recalling of Odia characters (vowels and consonants) in image form of size 30 X 30 on Hopfield network with different noise percentages. At the same time, the performance of recognition accuracy has been observed by partitioning the dataset into training and a different testing dataset with k-fold cross-validation method in the second experimental attempt. The simulation results obtained in this study express the comparative performance of the network for recalling of stored patterns and recognizing a new set of testing patterns with various noise percentages for different learning rules.