Finger vein image quality evaluation using support vector machines

Lu Yang; Gongping Yang; Yilong Yin; Rongyang Xiao

doi:10.1117/1.OE.52.2.027003

18 February 2013 Finger vein image quality evaluation using support vector machines

Lu Yang, Gongping Yang, Yilong Yin, Rongyang Xiao

Author Affiliations +

Optical Engineering, Vol. 52, Issue 2, 027003 (February 2013). https://doi.org/10.1117/1.OE.52.2.027003

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.

Citation Download Citation

Lu Yang, Gongping Yang, Yilong Yin, and Rongyang Xiao "Finger vein image quality evaluation using support vector machines," Optical Engineering 52(2), 027003 (18 February 2013). https://doi.org/10.1117/1.OE.52.2.027003

Published: 18 February 2013

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