A Simple and Efficient Method for Finger Vein Recognition

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.

Finger Vein Recognition Using DenseNet with a Channel Attention Mechanism and Hybrid Pooling

“…Similarly, it achieves a lower EER score in the FVUSM (0.03%), UTFVP (0.43%), and MMCBNU_6000 (1.80%) datasets. In comparison to other existing methodologies, such as deep segmentation (DS) [4], BACS-LBP [6], Lu et al [7], BMSU-LBP [9], and FVR-Net [24], SE-DenseNet-HP outperforms them by consistently achieving superior EER performance. Note that these methodologies exhibit varying degrees of EERs across different datasets, whereas SE-DenseNet-HP consistently stands out with its lower EER values across all datasets, validating the effectiveness of our proposed finger vein recognition system.…”

“…Table 4 presents an EER comparison of SE-DenseNet-HP with FVR-Net, deep segmentation (DS) [4], BACS-LBP [6], Lu et al [7], and BMSU-LBP [9]. EER values for DS, BACS-LBP, Lu et al and BMSU-LBP were adapted from [4,6,7,9] respectively. When evaluated on the HKPU dataset, SE-DenseNet-HP surpasses other methods by achieving an EER of 1.81%.…”

“…Even though biometric systems that utilize extrinsic characteristics are less susceptible to theft than conventional methods, they are vulnerable in practice to forged input, raising privacy and security concerns [4]. In contrast, biometric systems use intrinsic characteristics such as finger veins and palm veins that are hidden beneath the skin, which are difficult to forge [5][6][7]. Particularly, finger vein authentication systems have demonstrated promising applications in consumer electronics, banking, and airports [8].…”

Design and Implementation of a Self-Supervised Algorithm for Vein Structural Patterns Analysis Using Advanced Unsupervised Techniques

“…The table clearly explains that the CASIA Multispectral imagery dataset achieved a higher performance (PSNR = 25.19@after processing and RMSE = 0.055@after processing) than others. So far, most IQA approaches [23][24][25][26] published are based on subjective quality measures. This experiment potentially concludes that PSNR and obtaining a contrast image are compatible.…”

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

“…According to the different ways of finger vein feature extraction, finger vein recognition methods can be divided into 5 categories: Vein Pattern Methods,Feature Points Matching Methods,Statistical Characteristic Analysis Methods,Local Features Methods and Deep Learning Methods [1]. Vein pattern methods mainly use various algorithms to extract vein patterns from the pre-processed finger vein images and then compare the geometry or topology of the finger vein patterns to discern whether several finger vein images are from the same finger.…”