MMRAN: A novel model for finger vein recognition based on a residual attention mechanism

Liu, Weiye; Lu, Huimin; Wang, Yifan; Li, Yupeng; Qu, Zhenshen; Li, Yang

doi:10.1007/s10489-022-03645-7

Cited by 19 publications

(12 citation statements)

References 39 publications

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“…Adopting a similar approach, our method achieved a significant increase compared to Boucherit et al (2022) , with an almost 15% increase in accuracy. When data from both sessions are mixed, as in Das et al (2018) , Liu et al (2022) , Ma, Wang & Hu (2023) configuration, our method achieves similar results.…”

Section: Methodsmentioning

confidence: 73%

“…However, the proposed pipeline not only has a large number of parameters but also includes vigorous preprocessing of the input thereby making the proposed work complex. In Liu et al (2022) , the proposed fusion residual attention block includes a main path that extracts features at multiple scales and a guided attention path corresponding to the main path feature map. Venous features extracted at different learning stages through these two pathways are integrated using a multistage residual attention scheme.…”

Section: Introductionmentioning

confidence: 99%

“…This approach leads to networks with a significant number of parameters and, consequently, a high computational cost. In more recent works, there has been a shift towards utilizing various types of optimized convolutional layers to give rise to lightweight finger vein recognition networks ( Liu et al, 2022 ; Chai et al, 2022 ; Yang et al, 2022 ). Our primary contribution in this work is also in this aspect of optimization through (re)design of the convolutional blocks to enhance network performance and reduce parameter count.…”

Section: Introductionmentioning

confidence: 99%

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

Tahir,

Rosdi

2024

PeerJ Computer Science

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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.

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Section: Methodsmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tahir,

Rosdi

2024

PeerJ Computer Science

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“…[31] proposed a local attention Transformer network based on the full view, and liu et al. [32] designed a multiscale and multistage residual attention network. Both attention networks have been used for finger vein recognition with good results.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Song et al [29] used the deep DenseNet model to improve the recognition performance. Besides, embedding attention mechanisms in the network structure is also a way to improve the network performance [30], for example, Qin et al [31] proposed a local attention Transformer network based on the full view, and liu et al [32] designed a multiscale and multistage residual attention network. Both attention networks have been used for finger vein recognition with good results.…”

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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Federated learning for biometric recognition: a survey

Guo,

Mu,

Liu

et al. 2024

Artif Intell Rev

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Recently, deep learning (DL) has achieved great success in biometric recognition. The application of DL has also led to a high demand for biometric data. However, as people attach more importance to privacy protection, biometric data have become increasingly difficult to obtain and access, leading to significant limitations in the development and application of DL-based biometric recognition. Federated learning (FL), a distributed learning technique with privacy protection, provides a potential solution to this problem. Several researchers have attempted to integrate FL into biometric recognition. These studies have shown that the introduction of FL not only solves the conflict between privacy and accessibility of biometric data but also improves the accuracy and generalizability of local recognition systems. Therefore, the combination of FL and biometric recognition techniques has become a new research hotspot. In this survey, we comprehensively review the latest advances regarding the application of FL to biometric recognition, biometric presentation attack detection and the related fields to provide new researchers with a quick and systematic overview of this emerging cross-disciplinary field. This paper also summarizes the future opportunities and challenges of this field. To our knowledge, this is the first survey that systematically organizes and analyses federated biometric recognition and related fields to provide suggestions and references for future research.

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MMRAN: A novel model for finger vein recognition based on a residual attention mechanism

Cited by 19 publications

References 39 publications

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

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

Finger vein recognition based on lightweight convolutional attention model

Federated learning for biometric recognition: a survey

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