Fingerprint Identification System Using Wavelet Transform And Artificial Neural Network

This research implements two methods to perform fingerprint processing. The first method is the wavelet transformation used for the fingerprint feature extraction. The second method is the back propagation artificial neural network algorithm used for the process of fingerprint identification. Fingerprint data sample is obtained from the website at http://www.bias.csr.unibo.it/fvc2004.databases.asp which can be downloaded for free. Wavelet transformation function to extract fingerprint characteristics by doing the decomposition for 4 levels. From the result of the decomposition, the coefficient having the greatest magnitude (low-frequency images) for 8x8 pixels is taken. This Characteristic is stored into the database My SQL to be inputs for back propagation artificial neural network. They are 64 input neurons. Input system is the fingerprints image and as the fingerprint identification of the owner (id, name, and address.). At the beginning of processing, the fingerprint is converted to be grayscale image. Then, it is changed to be YIQ color space, and only luminance Y as the gray factor of image is taken. To find the best combination of algorithm parameter of the artificial neural network, it is done by testing combination of parameters repeatedly. As the result, the best parameter combination with learning rate = 0.1, hidden layer neurons= 125 neurons with 15 fingerprint data is good .This parameter produces the good introduction of back propagation artificial neural network for 86.6%. From the best result of parameter combination, it is used to test the influence of the number of fingerprints toward the recognition. The result of the experiment shows that artificial neural network performance decreases along with the increasing number of fingerprint data being tested.