Title: Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

Authors: Mohanad Abukmeil; Gian Luca Marcialis

Addresses: Department of Computer Science, University of Milan, Via Giovanni Celoria, 18, 20133 Milano MI, Italy ' Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, I-09123 Cagliari, Italy

Abstract: In this paper, we investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Although the literature proposed several approaches for palmvein recognition, the palmvein performance is still affected by identification and verification errors. As well-known, palmveins are usually described by line-based methods which enhance the vein flow. This is claimed to be unique from person to person. However, palmvein images are also characterised by texture that can be pointed out by textural features, which relies on recent and efficient hand crafted algorithms such as local binary patterns, local phase quantisation, local tera pattern, local directional pattern, and binarised statistical image features (LBP, LPQ, LTP, LDP and BSIF, respectively), among others. Finally, they can be easily managed at feature-level fusion, when more than one sample can be acquired for recognition. Therefore, multi-snapshot fusion can be adopted for exploiting these features complementarity. Our goal is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark dataset.

Keywords: vein recognition; biometrics security; textural representations; fusion; limited data learning; machine learning.

DOI: 10.1504/IJBM.2022.119547

International Journal of Biometrics, 2022 Vol.14 No.1, pp.20 - 45

Accepted: 18 May 2020
Published online: 09 Dec 2021 *

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