Finger Vein Recognition by Generating Code
Zhongxia Zhang, Mingwen Wang

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…
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Taxonomy
TopicsBiometric Identification and Security · Dermatoglyphics and Human Traits · Handwritten Text Recognition Techniques
