The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.

Das, R., Piciucco, E., Maiorana, E., Campisi, P. (2019). Convolutional Neural Network for Finger-Vein-Based Biometric Identification. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 14(2), 360-373 [10.1109/TIFS.2018.2850320].

Convolutional Neural Network for Finger-Vein-Based Biometric Identification

Das, Rig;Piciucco, Emanuela;Maiorana, Emanuele;Campisi, Patrizio
2019-01-01

Abstract

The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.
2019
Das, R., Piciucco, E., Maiorana, E., Campisi, P. (2019). Convolutional Neural Network for Finger-Vein-Based Biometric Identification. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 14(2), 360-373 [10.1109/TIFS.2018.2850320].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/338590
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 230
  • ???jsp.display-item.citation.isi??? 180
social impact