A Multimodal Finger-Based Recognition Method Based on Granular Computing

摘要

Finger-based biometrics is widely used in identity authentication. In this paper, viewing fingerprint (FP), finger-knuckle-print (FKP) and finger-vein (FV) as the constitutions of finger trait, a new multimodal finger-based recognition scheme is proposed based on granular computing (GrC). First, the ridge texture features of FP, FV and FKP are extracted using the feature extraction scheme of Orientation coding and Magnitude coding (OrientCode& MagCode) which combines orientation and magnitude information extracted by Gabor filtering. Combining the OrientCode and MagCode feature maps in a color-based manner respectively, we then constitute the original feature object set of a finger. To represent finger feature effectively, they are granulated at three levels of information granularity in a bottom-up manner based on GrC. Moreover, a top-down matching method is proposed to test the performance of the multilevel feature granules. Experimental results show that the proposed method achieves higher accuracy recognition rate in multimodal finger-based recognition.