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A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature

A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature

Junying Zeng, Yao Chen, Yikui Zhai, Junying Gan, Wulin Feng, Fan Wang
Copyright: © 2019 |Volume: 15 |Issue: 1 |Pages: 16
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781522564225|DOI: 10.4018/IJEIS.2019010106
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MLA

Zeng, Junying, et al. "A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature." IJEIS vol.15, no.1 2019: pp.100-115. http://doi.org/10.4018/IJEIS.2019010106

APA

Zeng, J., Chen, Y., Zhai, Y., Gan, J., Feng, W., & Wang, F. (2019). A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature. International Journal of Enterprise Information Systems (IJEIS), 15(1), 100-115. http://doi.org/10.4018/IJEIS.2019010106

Chicago

Zeng, Junying, et al. "A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature," International Journal of Enterprise Information Systems (IJEIS) 15, no.1: 100-115. http://doi.org/10.4018/IJEIS.2019010106

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Abstract

Inferior finger vein images would seriously alter the completion of recognition systems. A modern finger-vein recognition technique combined with image quality assessment is developed to overcome those drawbacks. By the quality assessment, this article can discard the inferior images and retain the superior images which are then transferred to the recognition system. Different from previous methods, this article assesses the quality features of the image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. In light of this purpose, the quality assessment is implemented: first, the finger vein image is automatically annotated; second, the finger vein image is cut into image blocks to expand the training set; third, the average quality score of multiple image blocks from an image is the final quality score of the image in the course of testing. Next, the Histogram of Oriented Gradients (HOG) features are extracted from the four transformed high-quality sub-images, whose features are cascaded into the multi-scale HOG feature of an image. Finally, two modules, the quality assessment module using Convolutional Neural Networks (CNN) and finger vein recognition module which make full use of multi-scale HOG, are perfectly combined in this article. The test results have demonstrated that light-CNN can identifies inferior and superior images accurately and the multi-scale HOG is feasible and effective. What's more, this article can see the robustness of this combined method in this article.

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