کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535847 | 870392 | 2012 | 6 صفحه PDF | دانلود رایگان |

Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion.
► Proposed a new fingerprint-fingervein based biometric method.
► Extracted the two biometric features using an unified Gabor framework.
► Proposed a novel supervised local-preserving canonical correlation analysis method.
Journal: Pattern Recognition Letters - Volume 33, Issue 5, 1 April 2012, Pages 623–628