A Secure Bio- Metric Fingerprint Recognition using Neural Network

Artificial Neural Network are different means of prediction, optimization and recognition. 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. 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 images and that way provides the insights for developing a system which requires these samples for verification and authorization. A system is designed to provide authentication decision using the fingerprint inputs can be a reliable means of verification. Such a system designed using ANN and using fingerprint inputs is described here. Experimental results show that the system is reliable enough for considering it as a part of a verification mechanism.

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An application of fuzzy logic and neural network to fingerprint recognition

NSIP 2005. Abstracts. IEEE-Eurasip Nonlinear Signal and Image Processing, 2005. ◽

10.1109/nsip.2005.1502244 ◽

2005 ◽

Author(s):

Ching-Tang Hsieh ◽

Chia-Shing Hu

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Fingerprint Recognition

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Neural Networks for Fingerprint Recognition

Neural Computation ◽

10.1162/neco.1993.5.3.402 ◽

1993 ◽

Vol 5 (3) ◽

pp. 402-418 ◽

Cited By ~ 70

Author(s):

Pierre Baldi ◽

Yves Chauvin

Keyword(s):

Neural Network ◽

Neural Networks ◽

Data Base ◽

Error Rate ◽

Fingerprint Recognition ◽

Network Algorithm ◽

The Neural Network ◽

Neural Network Algorithm

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.

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Drop Fingerprint Recognition Method Based on Cluster Analysis and BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2099 ◽

2014 ◽

Vol 543-547 ◽

pp. 2099-2102 ◽

Cited By ~ 1

Author(s):

Qing Song ◽

Dan Qing Du ◽

Lu Yang ◽

Gao Jie Meng ◽

Xue Fei Mao

Keyword(s):

Neural Network ◽

Cluster Analysis ◽

Bp Neural Network ◽

Liquid Drop ◽

Recognition Accuracy ◽

Fingerprint Recognition ◽

High Similarity ◽

Recognition Method ◽

Accuracy Rate ◽

Accuracy Rates

The eigenvalues of some liquid drop fingerprints are of high similarity, which decreases the recognition accuracy rates of BP neural network. In order to solve this problem, recognition method based on cluster analysis and BP neural network is proposed in this paper. Cluster analysis is used to classify liquid samples according to the similarity of eigenvalues and narrow the recognition range for samples under study. The experimental results have proved that this method is able to increase the recognition accuracy rate from 83.42% to 99.83%.

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