Feature Extraction for Finger-Vein-Based Identity Recognition

Sidiropoulos, George K.; Kiratsa, Polixeni; Chatzipetrou, Petros; Papakostas, George A.

doi:10.3390/jimaging7050089

Cited by 30 publications

(14 citation statements)

References 116 publications

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“…For solutions using a single biometric trait, one of the most intensively developed areas is the use of the vascular system of the fingers of a human hand (FVS, finger vascular system) [ 6 ]. In this case, the use of a special apparatus is necessary for data acquisition: an illuminator and a camera/sensor for near-infrared imaging have to be utilized at a minimum.…”

Section: Introductionmentioning

confidence: 99%

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging

Fiołka

Bernacki

Farah

2023

Sensors

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Personal identification using analysis of the internal and external characteristics of the human finger is currently an intensively developed topic. The work in this field concerns new methods of feature extraction and image analysis, mainly using modern artificial intelligence algorithms. However, the quality of the data and the way in which it is obtained determines equally the effectiveness of identification. In this article, we present a novel device for extracting vision data from the internal as well as external structures of the human finger. We use spatially selective backlight consisting of NIR diodes of three wavelengths. The fast image acquisition allows for insight into the pulse waveform. Thanks to the external illuminator, images of the skin folds of the finger are acquired as well. This rich collection of images is expected to significantly enhance identification capabilities using existing and future classic and AI-based computer vision techniques. Sample data from our device, before and after data processing, have been shared in a publicly available database.

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Section: Introductionmentioning

confidence: 99%

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging

Fiołka

Bernacki

Farah

2023

Sensors

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“…In recent years, finger vein recognition has become a vital deployment system in identity information security due to its high security, non-contact, and in vivo sampling characteristics [1][2][3]. It is well known that the performance of finger vein recognition systems is influenced by the accuracy of feature extraction [4][5][6]. According to the different feature extraction methods, we broadly classify the existing extraction methods into traditional methods and methods of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Finger vein recognition based on lightweight convolutional attention model

Zhang

Wang

2023

IET Image Processing

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Convolutional neural networks are a hot research topic in finger vein recognition. However, most existing research focuses on increasing the depth and width of the convolutional neural network to improve the network's performance, which has a specific requirement on the computer's computational power. To reduce the computational burden, a lightweight convolutional attention model (LCAModel) is proposed for finger vein recognition to achieve more accurate visual structure capture by exploiting the sensitivity of the attention mechanism to features. First, the attentional model (AModel) is proposed to extract representative features of images, which mainly utilizes the adaptive mapping on space and channels of the convolutional block attention module (CBAM) to make features distinguishable. In addition, considering the integrity of the features, a convolutional model (CModel) is designed to supplement the features of AModel. Finally, the features obtained from AModel and CModel are fused using an adaptive weighting mechanism to make the features more complete. Here, the obtained features are provided into a support vector machine (SVM) for image classification. The experiments are carried out on two publicly available databases, demonstrating that the proposed network structure requires less computing power and performs better.

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“…Finger vein recognition is a bioassay technology, and the finger vein characteristics of different finger individuals are unique, which enables the high-accuracy identification of a large number of users [ 3 ]. In addition, as a non-contact biometric identification method, finger vein recognition is clean and hygienic.…”

Section: Introductionmentioning

confidence: 99%

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

Duan

Zhou

et al. 2022

Sensors

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In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

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Feature Extraction for Finger-Vein-Based Identity Recognition

Cited by 30 publications

References 116 publications

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging

Finger vein recognition based on lightweight convolutional attention model

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

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