sift based vein recognition models: analysis and improvement

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ID: 213961
2017
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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.
Reference Key
wang2017computationalsift Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Guoqing Wang;Jun Wang
Journal advanced functional materials
Year 2017
DOI
10.1155/2017/2373818
URL
http://dx.doi.org/10.1155/2017/2373818
Keywords
medical informatics computer applications to medicine

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