An Efficient Approach for Neural Network Based Fingerprint Recognition by Using Core, Delta, Ridge Bifurcation and Minutia

Author(s):  
Jitendra Singh Sengar ◽  
Jasvinder Pal Singh ◽  
Niresh Sharma
2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad

Author(s):  
TRIPTI RANI BORAH ◽  
Kandarpa Kumar Sarma ◽  
PRAN HARI TALUKDAR

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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