Fingervein Sample Image Quality Assessment using Natural Scene Statistics
Author:
Affiliation:
1. University of Salzburg,Visual Computing and Security Lab (VISEL),Department of Artificial Intelligence & Human Interfaces,Salzburg,Austria
Funder
Austrian Science Fund
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9896866/9896958/09896974.pdf?arnumber=9896974
Reference21 articles.
1. A high compatibility finger vein image quality assessment system based on deep learning
2. Finger-vein Image Quality Assessment Based on light-CNN
3. Longitudinal Finger Rotation—Deformation Detection and Correction
4. Making image quality assessment robust
5. No-Reference Image Quality Assessment in the Spatial Domain
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