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Journal of Electrical and Computer Engineering
Volume 2016 (2016), Article ID 7913170, 7 pages
http://dx.doi.org/10.1155/2016/7913170
Research Article

Spatial Circular Granulation Method Based on Multimodal Finger Feature

Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, CAUC, Tianjin 300300, China

Received 2 December 2015; Revised 3 February 2016; Accepted 14 March 2016

Academic Editor: Sook Yoon

Copyright © 2016 Jinfeng Yang et al. 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-based personal identification has become an active research topic in recent years because of its high user acceptance and convenience. How to reliably and effectively fuse the multimodal finger features together, however, has still been a challenging problem in practice. In this paper, viewing the finger trait as the combination of a fingerprint, finger vein, and finger-knuckle-print, a new multimodal finger feature recognition scheme is proposed based on granular computing. First, the ridge texture features of FP, FV, and FKP are extracted using Gabor Ordinal Measures (GOM). Second, combining the three-modal GOM feature maps in a color-based manner, we then constitute the original feature object set of a finger. To represent finger features effectively, they are granulated at three levels of feature granules (FGs) in a bottom-up manner based on spatial circular granulation. In order to test the performance of the multilevel FGs, a top-down matching method is proposed. Experimental results show that the proposed method achieves higher accuracy recognition rate in finger feature recognition.