Category-preserving binary feature learning and binary codebook learning for finger vein recognition

Liu, Haiying; Yang, Geng; Yin, Yilong

doi:10.1007/s13042-020-01143-1

Cited by 7 publications

(7 citation statements)

References 44 publications

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“…Weighted vein indexing [15] 99.93% CPBFL-BCL [2] 99.98% This Paper 100% Compared with classical networks, such as AlexNet ResNet-18, Basic CNN, the reason for the high accuracy of our scheme is that the weights are assigned adaptively through the attention mechanism, which highlights the important detail information of the image and the extracted features are more distinguishable. The network layer setup in literature [8] is similar to our model structure, but it needs to extract curvature by Gaussian template and use certain methods to extract feature images as input to CNN, which is time costly and computationally not small.…”

Section: This Paper 100%mentioning

confidence: 99%

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Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Zhang¹,

Wang²

2022

Preprint

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Convolutional neural networks have become a popular research in the field of finger vein recognition because of their powerful image feature representation. However, most researchers focus on improving the performance of the network by increasing the CNN depth and width, which often requires high computational effort. Moreover, we can notice that not only the importance of pixels in different channels is different, but also the importance of pixels in different positions of the same channel is different. To reduce the computational effort and to take into account the different importance of pixels, we propose a lightweight convolutional neural network with a convolutional block attention module (CBAM) for finger vein recognition, which can achieve a more accurate capture of visual structures through an attention mechanism. First, image sequences are fed into a lightweight convolutional neural network we designed to improve visual features. Afterwards, it learns to assign feature weights in an adaptive manner with the help of a convolutional block attention module. The experiments are carried out on two publicly available databases and the results demonstrate that the proposed method achieves a stable, highly accurate, and robust performance in multimodal finger recognition.

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Section: This Paper 100%mentioning

confidence: 99%

“…Traditional identification technology has been difficult to meet people's needs, so it is necessary to develop biometric technology with higher security. Finger veins are hidden under the skin of the fingers [1], [2]. Compared with other biometric features, its structure is complex and not available under visible light.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Zhang¹,

Wang²

2022

Preprint

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“…Collectors are usually portable and practical. According to [6], CPBFL works in two phases. Both the training phase and the testing phase use pixel difference vector (PDV) to extract features from the images.…”

Section: Finger Vein Recognition Finger Vein Recognition Has the Advantages Of Security And Privacymentioning

confidence: 99%

Impact on Biometric Identification Systems of COVID-19

Guo

2021

Scientific Programming

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COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.

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“…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]. Compared with other biometric technologies, a finger vein image has the following advantages for personal authentication: (1) Safety: vein pattern is an internal feature and not easy to replicate [6]; (2) Living body identification: only the vein in a living finger can be captured and further used in identification [7]; (3) Non-contact: the aging and wear of the skin surface can be ignored because finger veins are located in the subcutaneous layer of the skin.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Efficient Method for Finger Vein Recognition

Zhang

Wang

2022

Sensors

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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.

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Category-preserving binary feature learning and binary codebook learning for finger vein recognition

Cited by 7 publications

References 44 publications

Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Impact on Biometric Identification Systems of COVID-19

A Simple and Efficient Method for Finger Vein Recognition

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