Fusion of Partition Local Binary Patterns and Convolutional Neural Networks for Dorsal Hand Vein Recognition

Li, Kefeng; Liu, Quankai; Zhang, Guangyuan

doi:10.1007/978-3-030-86608-2_20

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…We searched many references and found no literature combining DL and HOG, but there are experiments with CNN and PLBP feature fusion. In [25], the authors designed three methods of CNN and PLBP feature fusion, namely serial fusion, decision fusion, and feature fusion. The decision fusion and feature fusion are the best, which are 0.34% higher than the CNN network without fusion.…”

Section: Other Deep Learning Vs Traditional Methods Discussionmentioning

confidence: 99%

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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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Section: Other Deep Learning Vs Traditional Methods Discussionmentioning

confidence: 99%

“…Recently, some researchers [25] achieved the current optimal results on the NCUT dataset using CNN and PLBP feature fusion. We adopted this idea and fused PLBP with ResNet for feature fusion, and the experimental results are shown in Table 6.…”

Section: Comparison With Other Researchersmentioning

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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“…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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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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Fusion of Partition Local Binary Patterns and Convolutional Neural Networks for Dorsal Hand Vein Recognition

Cited by 2 publications

References 15 publications

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

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

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

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