Vol. 3 Issue 4
Year:2015
Issue:Oct-Dec
Title:Enhanced Fingerprint Image De-Noising Using Bi-Directional Recurrent Neural Network
Author Name:Deepika Bancchor and Siddharth Choubey
Synopsis:
Fingerprint Images (FPI) are always prone to be corrupted by various sources of noise during the capture of an image, i.e., acquisition period. This paper studies the implementation of Pixel Component Analysis (PCA) algorithm with Bi- Directional Recurrent Neural Network (BRNN) which will effectively de-noise the FPI images. BRNN enables compression of non-reusable fingerprint image data points during PCA execution and can transform vector co-ordinates in a rational manner. The duration of execution of the operation is also significantly reduced. The output of the proposed model has showed an optimized performance for de-noising of FPI images.
Year:2015
Issue:Oct-Dec
Title:Enhanced Fingerprint Image De-Noising Using Bi-Directional Recurrent Neural Network
Author Name:Deepika Bancchor and Siddharth Choubey
Synopsis:
Fingerprint Images (FPI) are always prone to be corrupted by various sources of noise during the capture of an image, i.e., acquisition period. This paper studies the implementation of Pixel Component Analysis (PCA) algorithm with Bi- Directional Recurrent Neural Network (BRNN) which will effectively de-noise the FPI images. BRNN enables compression of non-reusable fingerprint image data points during PCA execution and can transform vector co-ordinates in a rational manner. The duration of execution of the operation is also significantly reduced. The output of the proposed model has showed an optimized performance for de-noising of FPI images.
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