Palmprint identification performance improvement via patch-based binarized statistical image features - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Electronic Imaging Année : 2019

Palmprint identification performance improvement via patch-based binarized statistical image features

(1) , (1) , (1) , (2, 3) , (4)
1
2
3
4

Résumé

In the last few years, most works on palmprint recognition systems have been focused on developing a practical system that should have high performance in term of recognition accuracy, matching speed, and storage requirement. However, they have certain shortcomings, such as long computational time and sensitiveness to translation, illumination, and rotation. To handle these limitations, we present a simple and effective scheme to produce a meaningful local palmprint representation called patch binarized statistical image features descriptor (PBSIFD) for palmprint identification. The PBSIFD representation significantly exploits the power of the BSIF texture descriptor. In addition, the reduced version of PBSIFD called RPBSIFD is also obtained using whitened linear discriminant analysis. The proposed schemes are successfully applied to four widely used palmprint databases, including PolyU2D, PolyU2D/3D, IITD, and CASIA, and they are compared with recent approaches. It is shown that they outperform the existing methods. (C) 2019 SPIE and IS&T
Fichier non déposé

Dates et versions

hal-02456613 , version 1 (27-01-2020)

Identifiants

Citer

Salim Bendjoudi, Hocine Bourouba, Hakim Doghmane, Kamel Messaoudi, El-Bay Bourennane. Palmprint identification performance improvement via patch-based binarized statistical image features. Journal of Electronic Imaging, 2019, 28 (05), pp.053009. ⟨10.1117/1.JEI.28.5.053009⟩. ⟨hal-02456613⟩
47 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More