Mahmoud Alborzi et al./ Elixir Comp. Sci. & Engg. 41 (2011) 5797-5802 5797 Introduction Hopfield Network is one of the neural networks which have been used in the present research. Hopfield network in images can have useful applications. Fingerprint images which are used for identification may have noise. For example, it may be injured. In the present research, we study to what extent Hopfield Network can improve noisy or defective fingerprint images and be applied in fingerprint recognition process. Fingerprint images become binary after recalling to be calculated for Hopfield network studies. Hamming Distance is regarded as the best option due to application for binary data. Hamming Distance will find the closest similar image by studying differences between each image of input fingerprint and other images. Images of fingerprint Fingerprint of each person is a unique biological trait and can have effective applications for identification. Identification system based on biological traits should be distinguished and can be gathered and stable and bolometric trait of fingerprint has the mentioned specifications so that it has been regarded as valid evidence in judicial authorities (Daliri et al, 2007). Images of fingerprint are fingertips tissue pattern which have unevenness. Unevenness of the fingertip is due to wrinkles (Jean et al, 1997). According to analysis and study of wrinkles in fingertip, we can adjust identify recognition system based on this biological trait. Preprocessing operations on images of fingerprints images: In the present research, 40 fingerprints images have been used as model set for Hopfield Network learning. All images of fingertips are converted to black and white format and then size of all images is equal. Primary images of fingertip are in large dimensions which are difficult to calculate. For this reason, images will be calculated in smaller sizes. Background of fingertips is removed and contrast of the images is increased so that images of fingertips are made more evident. Stages of preprocessing operations on one of the fingerprint images are shown in Figure 1. After performing preprocessing on the model set images, Hopfield operations will be performed. Figure 1: Preprocessing operations on the fingerprint images Recall of fingerprint images In the present research, all operations on images have been done in MATLAB software. In this software, the related matrix will be specified after recall of the images. Matrix elements of fingerprint images are determined on the basis of gray spectrum zero to 255 belonging to each one of the image pixels. In the next stage , images are binary and because the studied method is Hopfield network and all studied elements in Hopfield network should be +1 and -1, therefore, all zeros are converted to -1 and +1s remain unchanged. In the following Figure, matrixes of fingerprint image are shown. In the first stage, all elements of matrix are between zero and 255 and are zero and 1 in the next stage and finally between -1 and +1. Figure 2: Fingerprint images matrices Tele: E-mail addresses: toloie@gmail.com, toloie@srbiau.ac.ir © 2011 Elixir All rights reserved Application of hopfield network in improvement of fingerprint recognition process Mahmoud Alborzi 1 , Abbas Toloie- Eshlaghy 1 and Dena Bazazian 2 1 Industrial management department, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Information technology management department, Science and Research Branch, Islamic Azad University, Tehran, Iran. ABSTRACT Hopfield Network is able to convert noisy data provided to the network because it can act as content addressable memory. Hopfield Network can convert images of noisy fingerprint to the noiseless images or the images with the minimum noise by training through receivable models set. In this research, fingerprint recognition process has been performed through Hamming Distance and effect of use of Hopfield network on fingerprint recognition process has been mentioned. In case those Hamming Distance operations are performed after Hopfield Networks processing, error of fingerprint recognition will be reduced. © 2011 Elixir All rights reserved. ARTICLE INFO Article history: Received: 25 September 2011; Received in revised form: 18 November 2011; Accepted: 29 November 2011; Keywords Fingerprint image, Noise, Hopfield Network, Hamming Distance. Elixir Comp. Sci. & Engg. 41 (2011) 5797-5802 Computer Science and Engineering Available online at www.elixirpublishers.com (Elixir International Journal)