Capsule Network for Finger-Vein-based Biometric Identification

Gumusbas, Dilara; Yıldırım, Tülay; Kocakulak, Mustafa; Acır, Nurettin

doi:10.1109/ssci44817.2019.9003019

Cited by 16 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of a weak classifier affects the performance of the ensemble model. In previous work, CNNs were widely used in finger vein recognition and achieved good recognition performance, such as Wang et al ( 2017 ), Gumusbas et al ( 2019 ), Avci et al ( 2019 ), Gumusbas et al ( 2019 ), and Zhang J. et al ( 2019 ). In this article, we choose the CNN model as the independent weak classifier, and each CNN is trained by one feature map, as shown in Figure 1 .…”

Section: Ensemble Learning For Sspp Finger-vein Recognitionmentioning

confidence: 99%

“…Some researchers brought them into finger-vein recognition. For example, deep learning approaches are employed for vein image segmentation (Liskowski and Krawiec, 2016 ; Qin et al, 2019 ; Yang et al, 2019 ; Shaheed et al, 2022a ), quality assessment of vein image (Qin and Yacoubi, 2015 ; Qin and El-Yacoubi, 2018 ), fuzzy networks (Liu H. et al, 2022 ; Lu et al, 2022 ; Muthusamy and Rakkimuthu, 2022 ), and finger-vein recognition (Wang et al, 2017 ; Avci et al, 2019 ; Gumusbas et al, 2019 ; Zhang J. et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep ensemble learning method for single finger-vein identification

Liu

Qin²,

Song³

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.

show abstract

Section: Ensemble Learning For Sspp Finger-vein Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep ensemble learning method for single finger-vein identification

Liu

Qin²,

Song³

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…This study was the first to evaluate its model on four different publicly available databases, achieving stable and high accuracy across all of them. Gumusbas et al (2019) in their work leveraged capsule networks for their advantages in rotational and translational invariance to extract robust finger vein features for identification. In contrast to CNNs, capsule networks utilize vector representations of features stored in capsule layers instead of numerical activation values.…”

Section: Introductionmentioning

confidence: 99%

“…Executing this operation with multiple kernels involves quadruple-component multiplication resulting in quadratic complexity ( Chollet, 2017 ; Freeman, Roese-Koerner & Kummert, 2018 ), which comes with nontrivial computational cost. A common trend observed in several early architectures for finger vein recognition ( Das et al, 2018 ; Boucherit et al, 2022 ; Gumusbas et al, 2019 ; Tamang & Kim, 2022 ; Yang et al 2020 ; Hao, Fang & Yang, 2020 ) is the performance of feature extraction through multiple successive layers without any form of spatial downscaling. This approach leads to networks with a significant number of parameters and, consequently, a high computational cost.…”

Section: Introductionmentioning

confidence: 99%

FV-EffResNet: an efficient lightweight convolutional neural network for finger vein recognition

Tahir,

Rosdi

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Several deep neural networks have been introduced for finger vein recognition over time, and these networks have demonstrated high levels of performance. However, most current state-of-the-art deep learning systems use networks with increasing layers and parameters, resulting in greater computational costs and complexity. This can make them impractical for real-time implementation, particularly on embedded hardware. To address these challenges, this article concentrates on developing a lightweight convolutional neural network (CNN) named FV-EffResNet for finger vein recognition, aiming to find a balance between network size, speed, and accuracy. The key improvement lies in the utilization of the proposed novel convolution block named the Efficient Residual (EffRes) block, crafted to facilitate efficient feature extraction while minimizing the parameter count. The block decomposes the convolution process, employing pointwise and depthwise convolutions with a specific rectangular dimension realized in two layers (n × 1) and (1 × m) for enhanced handling of finger vein data. The approach achieves computational efficiency through a combination of squeeze units, depthwise convolution, and a pooling strategy. The hidden layers of the network use the Swish activation function, which has been shown to enhance performance compared to conventional functions like ReLU or Leaky ReLU. Furthermore, the article adopts cyclical learning rate techniques to expedite the training process of the proposed network. The effectiveness of the proposed pipeline is demonstrated through comprehensive experiments conducted on four benchmark databases, namely FV-USM, SDUMLA, MMCBNU_600, and NUPT-FV. The experimental results reveal that the EffRes block has a remarkable impact on finger vein recognition. The proposed FV-EffResNet achieves state-of-the-art performance in both identification and verification settings, leveraging the benefits of being lightweight and incurring low computational costs.

show abstract

“…Recently, some more powerful but complex network models, such as Siamese Network [ 46 ], GaborPCA Network [ 47 , 48 ], Convolutional Autoencoder [ 49 ], Capsule Network [ 50 ], DenseNet [ 51 , 52 ], Fully Convolutional Network (FCN) [ 53 , 54 ], Generative Adversarial Network (GAN) [ 55 , 56 , 57 ], and Long Short-term Memory (LSTM) Network [ 58 ], etc., also emerged in the field of FV image recognition. Especially for the GAN, which can not only achieve more robust vein patterns from low-quality FV images, but also generate a variety of synthetic FV samples.…”

Section: Introductionmentioning

confidence: 99%

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Yao

Song

et al. 2021

Sensors

View full text Add to dashboard Cite

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

show abstract

Capsule Network for Finger-Vein-based Biometric Identification

Cited by 16 publications

References 10 publications

A deep ensemble learning method for single finger-vein identification

A deep ensemble learning method for single finger-vein identification

FV-EffResNet: an efficient lightweight convolutional neural network for finger vein recognition

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Contact Info

Product

Resources

About