Finger Vein Recognition with Personalized Feature Selection

Sensors

Xiaoming Xi²  Gongping Yang¹  Yilong Yin² 

[1] School of Computer Science and Technology, Shandong University, Jinan 250101, China;

关键词: finger vein recognition;    feature extraction;    PHGTOG;    personalized feature selection;   

DOI  :  10.3390/s130911243

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.