ARTIFICIAL NEURAL NETWORK BASED FINGERPRINT RECOGNITION (original) (raw)

FINGERPRINT RECOGNITION USING ARTIFICIAL NEURAL NETWORK

Abstract- N Network s The ability of the ANN to learn given patterns makes them suitable for such applications. Fingerprint recognition is one such area that can be used as a means of biometric verification where the ANN can play a critical rule. An ANN can be configured and trained to handle such variations observed in the texture of the fingerprint.

Fingerprint Identification System Using Neural Networks

The use of fingerprint in biometric identification has been the most widely used authentication system. The uniqueness of the fingerprint for every human provides us with all we need for faultless identification. However, during the fingerprint scanning process, the image generated by the scanner may be slightly different during each scan. This paper puts the implementation of Artificial Neural Networks to provide an efficient matching algorithm for fingerprint authentication. Using the Back-Propagation technique, the algorithm works to match twelve fingerprint parameters and relate them to a unique number provided for each authorized user. Upon matching, the algorithm returns the best match for the given fingerprint parameters.

A Survey of Fingerprint Analysis using Artificial Neural Networks

Fingerprint Analysis is one of the efficient biometric tool which is highly used to verify the identity of the user. Artificial neural networks consist of various sets of neurons which resembles the neurons in the human brains. ANN is most widely used in the fingerprint analysis because of quick learning Techniques. Usually the ANN is trained to determine the characteristics that will be occurring in the fingerprint and extraction of those characteristics. In the training phase the extracted characterizations are compared with the templates that will be present in the stored database. A wavelet is used to divide a continuous signal into a scalable component, this technique of wavelets help to determine and differentiate the mordant characteristics of the fingerprint.

Biometric Identification Using Artificial Neural Network

Artificial Neural Networks (ANN) are the efficient means of prediction and recognition. The ability of the ANN to learn given patterns makes them suitable for such applications. Fingerprint, Face and Retina Recognition are the areas that can be used as a means of biometric verification where the ANN can play a critical rule. But faces of two twins can be of same look, so Fingerprint & Retina (F&R) are unique biometric pattern that can be used as a part of a verification system. An ANN can be configured and trained to handle such variations observed in the texture of the fingerprint & Retina. The specialty of the work is associated with the fact that if the ANN is configured properly it can tackle the variations in the fingerprint& retinal images and that way provides the insights for developing a system which requires the samples for verification and authorization. A system designed to provide authentication decision using the input can be a reliable means of verification. Such a system designed using ANN and using retina input is described here. Experimental results show that the system is reliable enough for considering it as a part of a verification mechanism.

A TECHNIQUE OF FINGERPRINT RECOGNITION USING ARTIFICIAL NEURAL NETWORK

This paper is present fingerprint recognition with the technique of artificial neural network. Fingerprint is widely accepted for personnel identification. In this technique we used back propagation algorithm. Firstly we used filter and histogram process of the input image than we trained artificial neural network on the input image.

FINGERPRINT IDENTIFICATION SYSTEM USING NEURAL NETWORK

Here I design and develop a pattern recognition system using Artificial Neural Network (ANN) and MATLAB that can recognize the type of image based on the features extracted from the choose image. Also I am comparing Backpropagation Neural Network and Cellular Neural Network. This system which can fully recognizing the types of the data had been add in the data storage or called as training data. The Graphic User Interface (GUI) in MATLAB toolbox is used. This is the alternative way to change the common usage of the MATLAB which are uses the command inserts at command window. From this kind of system, we just need to insert the features data or training data. The recognition done after we insert the test data. The system will recognize whether the output is match with the training data. Then output will produce a kind of graph that describes the feature of the data which is same as the training data. Index Terms: Matlab, Backpropagation Neural Network, Cellular Neural Network, Minutiae.

Neural Networks for Fingerprint Recognition

Neural Computation, 1993

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.

Performance of Fingerprint Enhancement and Classification Using Neural Network

INTERNATIONAL JOURNAL OF COMPUTER APPLICATION

Fingerprints are the best and most widely used form of biometric authentication. Everyone is known to have unique, immutable fingerprints. Fingerprints are secure to use because they do not change in one"s lifetime. Since fingerprints are unique, even between identical twins, they are perfect for various security uses. The goal of biometric authentication system is to compare two fingerprint images. The authentication in aadhaar card is done by this comparison. Human fingerprints are rich in details called minutiae, which can be used as identification marks for security purposes. Fingerprint authentication systems are based on local ridge features known as minutiae extraction, marking minutiae accurately and rejecting false ones. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. To get accurate minutiae, image enhancement techniques are employed prior to minutiae extraction. This paper presents techniquesto verify the identity of fingerprint. So the minutiae points of fingerprint images are classified using support vector machine algorithm for the identification of type of fingerprints.

Fingerprint Verification Based on Back Propagation Neural Network

CEAI, Vol.15, No.3 pp. 53-60, 2013, 2013

This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutiae's. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutiae points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase. Whatever, the degree of fingerprint rotation, the extraction features used to characterize fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate of fingerprint minutiae's. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. After that, the classification accuracy of the proposed approach is evaluated with Back Propagation Neural Network (BPNN). Extensive experiments prove that the fingerprint classification based on a novel features and BPNN classifier gives better results in fingerprint classification than several other features and methods. Finally the results of the proposed method are evaluated on the FVC 2002 database.