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Optical Engineering

Finger vein image quality evaluation using support vector machines
Author(s): Lu Yang; Gongping Yang; Yilong Yin; Rongyang Xiao
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Paper Abstract

In an automatic finger-vein recognition system, finger-vein image quality is significant for segmentation, enhancement, and matching processes. In this paper, we propose a finger-vein image quality evaluation method using support vector machines (SVMs). We extract three features including the gradient, image contrast, and information capacity from the input image. An SVM model is built on the training images with annotated quality labels (i.e., high/low) and then applied to unseen images for quality evaluation. To resolve the class-imbalance problem in the training data, we perform oversampling for the minority class with random-synthetic minority oversampling technique. Cross-validation is also employed to verify the reliability and stability of the learned model. Our experimental results show the effectiveness of our method in evaluating the quality of finger-vein images, and by discarding low-quality images detected by our method, the overall finger-vein recognition performance is considerably improved.

Paper Details

Date Published: 18 February 2013
PDF: 10 pages
Opt. Eng. 52(2) 027003 doi: 10.1117/1.OE.52.2.027003
Published in: Optical Engineering Volume 52, Issue 2
Show Author Affiliations
Lu Yang, Shandong Univ. (China)
Gongping Yang, Shandong Univ. (China)
Yilong Yin, Shandong Univ. (China)
Rongyang Xiao, Shandong Univ. (China)


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