Fingerprint Identification and Verification System by Minutiae Extraction Using Artificial Neural Network
The proposed Fingerprint Identification and verification System is biometric identification methodology that uses digital imaging technology to obtain, store, and analyze fingerprint data. Here we want introduced a new method for fingerprint identification technology by minutiae feature extraction using back-propagation algorithm. For an input image, the local ridge orientation is estimated and the region of interest is located. Then, ridges are extracted from the input image, refined to get rid of the small speckles and holes, and thinned to obtain 8-connected single pixel wide ridges. Minutiae are extracted from the thinned ridges and refined using some heuristics. A feature extractor finds minutia features such as ridge end, bifurcation, short ridge and spur from the input fingerprint images. The digital values of these features are applied to input of the neural network for training purpose. For fingerprint recognition, the verification part of the system identifies the fingerprint based training performance of the network. Finally experimental result shows that the number of recognized sample rate of our proposed method is 95% which is much better than the existing fingerprint verification system using artificial neural network (92.5%).. FINGERPRINTS have been in use for biometric recognition since long because of their high acceptability, immutability and individuality . Biometrics techniques are divided into two categories i.e. Physiological (fingerprints, face, iris, DNA, retina, voice, hand geometry, palm print, retinal scan etc.) and Behavioral (gait, signature etc). These physiological or behavioral Characteristics are used for human identification on the basis of their universality, uniqueness, permanence and collectability . Fingerprint is the oldest process to detect human identity. In a recently published World Biometric Market Outlook (2005-2008), analysts predict that the average annual growth rate of the global biometric market is more than 28%, by 2007. The technologies that would be included in this are fingerprint technology by 60%, facial & iris by 13%, keystroke by 0.5% and digital signature scans by 2.5% . So it can be state that automatic fingerprint identification system is an efficient method to recognize human identity. Here we propose a new method which will identify and verify fingerprint image using back propagation neural network with other attractive feature. 2 FINGERPRINT A fingerprint is the feature pattern of one finger (Figure 1). Each person has his own fingerprints with the permanent uniqueness
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