We describe a new multi-matcher biometric approach, using Knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon Transform and by Haar Wavelet, then these features are transformed by Non-Linear Fisher Transform. Finally, the matching process is based on Parzen Window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (near zero Equal Error Rate).
Nanni, L., Lumini, A. (2009). A multi-matcher system based on Knuckle-based features. NEURAL COMPUTING & APPLICATIONS, 18, 87-91 [10.1007/s00521-007-0160-4].
A multi-matcher system based on Knuckle-based features
NANNI, LORIS;LUMINI, ALESSANDRA
2009
Abstract
We describe a new multi-matcher biometric approach, using Knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon Transform and by Haar Wavelet, then these features are transformed by Non-Linear Fisher Transform. Finally, the matching process is based on Parzen Window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (near zero Equal Error Rate).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.