Paper Info
Title
Efficient hardware implementation strategy for local normalization of fingerprint images
Abstract
Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.
Year
DOI
Venue
2019
10.1007/s11554-016-0625-8
Journal of Real-time Image Processing
Keywords
Field
DocType
Embedded image processing, FPGA, Biometrics
Background noise,Normalization (statistics),Computer science,Real-time computing,Artificial intelligence,Computer hardware,Computer vision,Segmentation,Local variance,Field-programmable gate array,Fingerprint,Biometrics,Hardware architecture
Journal
Volume
Issue
ISSN
16
4
1861-8219
Citations 
PageRank 
References 
3
0.40
6
Authors
4
Name
Order
Citations
PageRank
tariq khan1507.73
D. G. Bailey27116.88
Mohammad A. Khan39911.58
Yinan Kong46313.20