Paper Info
Title
SIFT Based Vein Recognition Models: Analysis and Improvement.
Abstract
Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER). Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.
Year
DOI
Venue
2017
10.1155/2017/2373818
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Kernel (linear algebra),Computer vision,Scale-invariant feature transform,Dynamic range,Computer science,Word error rate,Artificial intelligence,Invariant (mathematics),Feature transform,Detector,Vein recognition
Journal
2017
ISSN
Citations 
PageRank 
1748-670X
1
0.36
References 
Authors
14
2
Name
Order
Citations
PageRank
Guoqing Wang17517.84
Jun Wang29228736.82