Sensors 2013, 13(9), 11243-11259; doi:10.3390/s130911243

Finger Vein Recognition with Personalized Feature Selection

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Received: 25 May 2013; in revised form: 7 August 2013 / Accepted: 16 August 2013 / Published: 22 August 2013
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.
Keywords: finger vein recognition; feature extraction; PHGTOG; personalized feature selection
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MDPI and ACS Style

Xi, X.; Yang, G.; Yin, Y.; Meng, X. Finger Vein Recognition with Personalized Feature Selection. Sensors 2013, 13, 11243-11259.

AMA Style

Xi X, Yang G, Yin Y, Meng X. Finger Vein Recognition with Personalized Feature Selection. Sensors. 2013; 13(9):11243-11259.

Chicago/Turabian Style

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing. 2013. "Finger Vein Recognition with Personalized Feature Selection." Sensors 13, no. 9: 11243-11259.

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