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18 September 2019 Palmprint identification performance improvement via patch-based binarized statistical image features
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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.

© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Salim Bendjoudi, Hocine Bourouba, Hakim Doghmane, Kamel Messaoudi, and El-Bay Bourennane "Palmprint identification performance improvement via patch-based binarized statistical image features," Journal of Electronic Imaging 28(5), 053009 (18 September 2019).
Received: 26 April 2019; Accepted: 28 August 2019; Published: 18 September 2019


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