Shape-driven lightweight CNN for finger-vein biometrics

Chai, Tingting; Li, Jiahui; Prasad, Shitala; Qi, Lu; Zhang, Zhaoxin

doi:10.1016/j.jisa.2022.103211

Cited by 18 publications

(16 citation statements)

References 23 publications

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“…Although the network structure in [38] is simpler than the proposed method, the recognition rate is much lower than our method. Other literature, such as [35–37, 39, 40, 43, 47], shows from the network structures listed in Table 5 that these schemes are far more complex than the proposed scheme, with more learnable parameters and, obviously, greater demand on the equipment than the proposed scheme.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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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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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Unlike traditional custom features, neural network-based methods perform better in image processing due to adaptive feature learning capabilities [36,37]. In recent years, neural network-based approaches have been widely used in finger vein recognition [35,38].…”

Section: Related Workmentioning

confidence: 99%

Finger vein recognition based on lightweight convolutional attention model

Zhang

Wang

2023

IET Image Processing

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“…W. Liu et al [11] 98.58 5.85 LFVRN_CE + ACE [12] 99.09 4.93 Semi-PFVN [13] 94.67 3.35 LightFVN + ACE [14] 96.17 2.65 VGG-16 [29] 86.16 136.28 DenseNet-121 [30] 93.54 7.46 EfficientNet-B0 [31] 99.70 4.64 ILCNN 99.82 1. 23 To investigate the impact of different methods in the proposed ILCNN model on finger vein recognition, we conducted an ablation study to assess their effectiveness.…”

Section: Cir (%) Params (M)mentioning

confidence: 99%

Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System

Hsia

Chen

2023

Bioengineering

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Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs’ high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model’s performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance.

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“…Among the modalities in this category is the finger vein. Recently, they have attracted a lot of research as a novel biometric method ( Shaheed et al, 2022 ; Tamang & Kim, 2022 ; Chai et al, 2022 ; Song, Kim & Park, 2019 ; Lu, Xie & Wu, 2019 ; Hong, Lee & Park, 2017 ). This is mainly due to their uniqueness to each individual, high security, and insulation against disease or accidents.…”

Section: Introductionmentioning

confidence: 99%

“…However, the late fusion strategy requires more computational time because there are more classifiers to train. Inspired by the shape of finger vein images Chai et al (2022) in their work introduced a lightweight network by merging lower pre-trained layers of MobileNet with newly constructed layers. These new layers were initialized with rectangular convolution kernels, demonstrating slightly better accuracy compared to previous finger vein recognition networks using square kernels.…”

Section: Introductionmentioning

confidence: 99%

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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Shape-driven lightweight CNN for finger-vein biometrics

Cited by 18 publications

References 23 publications

Finger vein recognition based on lightweight convolutional attention model

Finger vein recognition based on lightweight convolutional attention model

Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System

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

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