Open AccessThis article is
- freely available
Finger Vein Recognition with Personalized Feature Selection
School of Computer Science and Technology, Shandong University, Jinan 250101, China
* Author to whom correspondence should be addressed.
Received: 25 May 2013; in revised form: 7 August 2013 / Accepted: 16 August 2013 / Published: 22 August 2013
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
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Xi, X.; Yang, G.; Yin, Y.; Meng, X. Finger Vein Recognition with Personalized Feature Selection. Sensors 2013, 13, 11243-11259.
Xi X, Yang G, Yin Y, Meng X. Finger Vein Recognition with Personalized Feature Selection. Sensors. 2013; 13(9):11243-11259.
Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing. 2013. "Finger Vein Recognition with Personalized Feature Selection." Sensors 13, no. 9: 11243-11259.