Abstract
Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images 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 tackle these problems, this paper proposes a new finger vein recognition algorithm 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 was performed using the matrix generated by blocking and averaging. The obtained codewords were 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-the-art methods and has competitive potential in performing the matching task.
Highlights
Finger vein recognition has emerged from a fairly new topic a few years ago to significant deployed systems and has demonstrated a reasonably good recognition performance [1,2]
A variety of methods have been proposed for finger vein recognition, which can be roughly divided into the following categories according to the different methods of feature extraction
The proposed finger vein code generation algorithm is simple in calculation, does not need complex segmentation algorithm, can overcome the problem of low image quality, and has stronger robustness to image noise
Summary
Finger vein recognition has emerged from a fairly new topic a few years ago to significant deployed systems and has demonstrated a reasonably good recognition performance [1,2]. It can capture the texture features under the blood vessels from different viewpoints such as palm side [3], dorsal side [4], and periphery of the finger [5]. The authors in [14] used a low-rank representation to extract as much noise-free discriminative information from finger vein images for more effective and robust finger vein recognition. This scheme can extract more important information from low-quality images
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