Real-Time Dorsal Hand Recognition Based on Smartphone

Sayed, Mohamed; Taha, Mohamed; Zayed, Hala H.

doi:10.1109/access.2021.3126709

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…The proposed method uses a transfer learning model using CNN models (DenseNet, ResNet) to extract and classify both features.  SAYED et al [32] proposed a real-time, effective dorsal hand identification system to attain high frame rates and good outcomes. A smartphone is utilized to gather our dataset and is attached to a contactless device that comprises an infrared LED and a universal serial bus (USB)camera.…”

Section: Related Workmentioning

confidence: 99%

Dorsal Hand Vein Image Recognition: A Review

A. Rajab1,

M. Hashim2

2023

JEPS

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Subcutaneous vascularization has become a new solution for identification management over the pastfew years. Systems based on dorsal hand veins are particularly promising for high-security settings. Thedorsal hand vein recognition system comprises the following steps: acquiring images from the databaseand preprocessing them, locating the region of interest, and extracting and recognizing information fromthe dorsal hand vein pattern. This paper reviewed several techniques for obtaining the dorsal hand veinarea and identifying a person. Therefore, this study just provides a comprehensive review of existingprevious theories. This model aims to offer the improvement in the accuracy rate of the system that wasshown in previous studies and to evaluate test samples and training samples for person identificationusing distance criteria or neural networks.

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Section: Related Workmentioning

confidence: 99%

Dorsal Hand Vein Image Recognition: A Review

A. Rajab1,

M. Hashim2

2023

JEPS

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“…Liu et al [ 8 ] presented an improved biometric graph matching method that included edge attributes for graph registration and a matching module to extract discriminating features. Sayed et al [ 9 ] proposed a dorsal hand recognition system working in real time to achieve good results with a high frame rate.…”

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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