Open Access Open Access  Restricted Access Subscription Access

Spatial Circular Granulation Method Based on Multimodal Finger Feature


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

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.
User
Notifications
Font Size

Abstract Views: 60

PDF Views: 0




  • Spatial Circular Granulation Method Based on Multimodal Finger Feature

Abstract Views: 60  |  PDF Views: 0

Authors

Jinfeng Yang
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, CAUC, Tianjin 300300, China
Zhen Zhong
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, CAUC, Tianjin 300300, China
Guimin Jia
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, CAUC, Tianjin 300300, China
Yanan Li
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, CAUC, Tianjin 300300, China

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.