FVSR-Net: an end-to-end Finger Vein Image Scattering Removal Network

Du, Shanshan; Yang, Jinfeng; Zhang, Haigang; Zhang, Bob; Su, Zhigang

doi:10.1007/s11042-020-09270-1

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…To assess the efficacy of Let-Net, we conducted a comparative analysis with the SOTA deep-learning-based FV identification models. The benchmark models include FV_CNN [ 8 ], a reference to a CNN architecture designed for vein identification; Fvras-net [ 9 ], an embedded FV identification system; FV code [ 36 ], a method employing FV code indexing; L-CNN [ 37 ], a lightweight CNN model; ArcVein [ 38 ], which introduces a novel loss function, Arcvein loss; FVSR-Net [ 39 ], a model integrating a bio-optical model with a multi-scale CNN E-Net; S-CNN [ 34 ], a novel shallow CNN model; FVT [ 7 ], a transformer-based deep model with pioneering experiments across nine datasets; L-S-CNN [ 40 ], a lightweight Siamese network with self-attention mechanism; and FVFSNet [ 16 ], a method that concurrently extracts FV features in the spatial and frequency dimensions. Employing EER as a metric, comparative experiments were conducted across the nine public FV datasets outlined in Section 4.1 , with the results presented in Table 1 .…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Li,

Gong,

Zheng

2024

Sensors

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FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Li,

Gong,

Zheng

2024

Sensors

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“…Pathak et al [26] proposed a CNN image restoration model based on contextual coding in which semantic restoration was achieved by reconstructing the standard pixel loss and adversarial loss to construct image gray values. Du et al [27] and Shand et al [28] used feature semantic compression and fusion to construct a feature network for image recognition, and achieved quite good restoration results on large datasets. However, the finger vein image database that such methods are applied to is small in size and low in resolution, and cannot meet the requirements of deep model training.…”

Section: Finger Vein Image Enhancement Based On Deep Featurementioning

confidence: 99%

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

Feng,

Mao

et al. 2024

Sensors

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With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image.

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“…To assess the efficacy of Let-Net, we conducted a comparative analysis with the SOTA deep-learning-based FV identification models. The benchmark models include FV_CNN [8], a reference to a CNN architecture designed for vein identification; Fvrasnet [9], an embedded FV identification system; FV code [36], a method employing FV code indexing; L-CNN [37], a lightweight CNN model; ArcVein [38], which introduces a novel loss function, Arcvein loss; FVSR-Net [39], a model integrating a bio-optical model with a multi-scale CNN E-Net; S-CNN [34], a novel shallow CNN model; FVT [7], a transformer-based deep model with pioneering experiments across nine datasets; L-S-CNN [40], a lightweight Siamese network with self-attention mechanism; and FVFSNet [16], a method that concurrently extracts FV features in the spatial and frequency dimensions. Employing EER as a metric, comparative experiments were conducted across the nine public FV datasets outlined in Section 4.1, with the results presented in Table 1.…”

Section: Comparison and Evaluation With Existing Fv Modelsmentioning

confidence: 99%

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Li,

Gong,

Zheng

2024

Preprint

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FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method Let-Net(Large kernel and attention mechanism Network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to broaden the receptive field and incorporates depthwise convolution with residual connections to reduce parameter count. Secondly, Let-Net integrates an attention mechanism to enhance the information flows in channels and spaces for more comprehensive and distinctive FV feature extraction. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

show abstract

FVSR-Net: an end-to-end Finger Vein Image Scattering Removal Network

Cited by 9 publications

References 32 publications

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

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