Finger Vein Recognition by Generating Code

TL;DR

This paper introduces a novel finger vein recognition method that generates compact codes without image segmentation, improving efficiency and accuracy over traditional feature extraction techniques.

Contribution

The proposed method eliminates the need for segmentation, simplifies computation, and produces smaller, more effective feature codes for finger vein recognition.

Findings

Outperforms state-of-the-art methods in accuracy

Reduces data redundancy in feature representation

Demonstrates robustness across multiple databases

Abstract

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images or patterns and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tack these problems, this paper proposes a new finger vein recognition by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then the centrosymmetric coding is performed by using the generated eigenmatrix. The obtained codewords are concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-theart methods and has competitive potential in performing the matching task.