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

TL;DR

This paper demonstrates that multi-snapshot fusion of various textural features significantly improves palmvein recognition accuracy, achieving high rates on a benchmark dataset.

Contribution

It introduces a multi-snapshot fusion approach combining multiple textural features for palmvein recognition, enhancing accuracy over existing methods.

Findings

High recognition rates achieved on benchmark dataset

Multi-snapshot fusion improves recognition accuracy

Textural features complement line-based methods

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 characterized 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 Quantization, Local Tera Pattern, Local directional Pattern, and Binarized 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 in this paper is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.