Dorsal Hand Vein Recognition Based on Transfer Learning with Fusion of LBP Feature

Gu, Gaojie; Bai, Peirui; Li, Hui; Liu, Qingyi; Han, Chao; Min, Xiaolin; Ren, Yande

doi:10.1007/978-3-030-86608-2_25

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Therefore, the proposed method achieved a higher accuracy than the original U-Net network model while significantly reducing the number of model parameters, making it suitable for deployment in embedded systems. (2) The experiments on the self-built dataset and the Jilin University dataset showed that the model could achieve a high accuracy in both complex and clean backgrounds and can meet the requirements of practical applications in real environments.…”

Section: Discussionmentioning

confidence: 94%

See 1 more Smart Citation

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net

Zhang

Zou²,

Deng

et al. 2023

Sensors

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In response to the difficulty of traditional image processing methods to quickly and accurately extract regions of interest from non-contact dorsal hand vein images in complex backgrounds, this study proposes a model based on an improved U-Net for dorsal hand keypoint detection. The residual module was added to the downsampling path of the U-Net network to solve the model degradation problem and improve the feature information extraction ability of the network; the Jensen–Shannon (JS) divergence loss function was used to supervise the final feature map distribution so that the output feature map tended to Gaussian distribution and improved the feature map multi-peak problem; and Soft-argmax is used to calculate the keypoint coordinates of the final feature map to realize end-to-end training. The experimental results showed that the accuracy of the improved U-Net network model reached 98.6%, which was 1% better than the original U-Net network model; the improved U-Net network model file was only 1.16 M, which achieved a higher accuracy than the original U-Net network model with significantly reduced model parameters. Therefore, the improved U-Net model in this study can realize dorsal hand keypoint detection (for region of interest extraction) for non-contact dorsal hand vein images and is suitable for practical deployment in low-resource platforms such as edge-embedded systems.

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Section: Discussionmentioning

confidence: 94%

“…In recent years, DHV recognition has gained much attention as an emerging biometric technology. Owing to its safety, accuracy, and effectiveness, more and more researchers are involved [ 1 , 2 , 3 , 4 ]. Lefkovits et al [ 5 ] presented a dorsal hand vein recognition method based on convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net

Zhang

Zou²,

Deng

et al. 2023

Sensors

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“…), but this information has a limited impact on depth features, so the recognition results of the model are not significantly improved after performing feature fusion. We also found this problem in [17], where the authors used a fusion of ResNet and LBP features and only achieved a 1.97% higher recognition rate than using ResNet alone. In addition, we conducted four sets of experiments on the NCUT dataset to determine how much HOG affects ResNet.…”

Section: Influence Of Convolution Blocksmentioning

confidence: 90%

“…The databases used in this paper are the dataset of the Shandong University of Science and Technology (SDUST) [17], the dataset of the Eastern Mediterranean University of Turkey (FYO) [18], the dataset of NCUT, and the fusion dataset (Fusion Dataset).…”

Section: Datasetmentioning

confidence: 99%

Recognition of Dorsal Hand Vein in Small-Scale Sample Database Based on Fusion of ResNet and HOG Feature

Zhang

et al. 2022

Electronics

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As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have few images. To solve the above problems, we propose a method based on the fusion of ResNet and Histograms of Oriented Gradients (HOG) features, in which the shallow semantic information extracted by primary convolution and HOG features are fed into the residual structure of ResNet for full fusion and, finally, classification. By adding Gaussian noise, the North China University of Technology dataset, the Shandong University of Science and Technology dataset, and the Eastern Mediterranean University dataset are extended and fused to from a fused dataset. Our proposed method is applied to the above datasets, and the experimental results show that our proposed method achieves good recognition rates on each of the datasets. Importantly, we achieved a 93.47% recognition rate on the fused dataset, which was 2.31% and 26.08% higher than using ResNet and HOG alone.

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“…Different from previous work, the mean curvature [5] and maximum curvature [6] are employed to detect the valley structure produced by vein pixels for feature extraction. Besides, the LBP [7] and the improved versions [8] are investigated to vein feature extraction.The experimental results show that the proposed approach can effectively improve the performance of finger vein recognition. Kova [9] investigates an adaptive Gabor filter based vein extraction approach.…”

Section: Related Workmentioning

confidence: 99%

VeinCnnformer: convolutional neural network based transformer for vein recognition

wang,

Qin,

Zhang

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Vein recognition has received increasing attention in recent years. Currently, the deep learning such transformer show robust capacity of feature representation and successfully applied for vein recognition task. Despite of its advances in vein recognition, but it may fail to capture the 2D structure and spatial local information within each patch for image-based vision tasks. In this paper, we propose a novel Convolutional neural network based transformer for vein recognition, termed VeinCnnformer to take advantage of convolutional operations and self-attention mechanisms for improving representation learning. Our VeinCnnformer roots in the Mixed Convolutional Attention Module (MCAM), which fuses local features and global representations. Specifically, we propose three modules, convolutional neural network(CNN) module, convolutional channel attention module, and transformer module. Secondly, the convolutional channel attention module and CNN module are combined with transformer module to form the MCAM. Thirdly, we stack multiple MCAM to obtain our VeinCnnformer for vein recognition. Finally, the experimental results on three public vein databases show that our VeinCnnformer outperforms the existing vein recognition approaches and achieves the state of the art recognition results.

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Dorsal Hand Vein Recognition Based on Transfer Learning with Fusion of LBP Feature

Cited by 6 publications

References 21 publications

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net

Recognition of Dorsal Hand Vein in Small-Scale Sample Database Based on Fusion of ResNet and HOG Feature

VeinCnnformer: convolutional neural network based transformer for vein recognition

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