Performance analysis on feature extraction using dorsal hand vein image

Vairavel, K. S.; Ikram, Nazarul; Mekala, Sreenivas

doi:10.1007/s00500-019-03991-8

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The experimental results are shown in Table 5. It can be seen from Table 5 that the recognition rate of using the PLBP and HOG algorithms alone not only does not achieve good results but also is much lower than the experimental results of other researchers [10] on the NCUT dataset. The reason for the analysis is that when other researchers conducted experiments on the NCUT dataset, they used the original dataset and did not use Gaussian noise to expand the dataset.…”

Section: Feature Fusion Robustness Experimentsmentioning

confidence: 71%

“…The convolved HOG feature is HOG_Feature, and then spatial feature fusion is performed with Feature_Map. The fusion method is shown in Equation (10), and the fusion method is shown in Formula (11). Then input the fused features into the ResNet residual block, and finally reduce the dimension of the feature map output by the ResNet residual block and input it into the fully connected layer for classification.…”

Section: Layer Namementioning

confidence: 99%

“…DHV identification on a single database includes two methods, namely traditional features and deep learning methods. In 2019, Vairavel et al [10] studied the recognition performance of three classical dense descriptors for DHV recognition, including Local Binary Pattern(LBP), HOG, and Weber local descriptor (WLD), and achieved good results on the Northern University of Technology(NCUT) [11] database. Liu et al [12] proposed an improved biometric map matching method in 2020, which achieved a 98.09% recognition rate on the Xi'an Jiaotong University (XJTU) database.…”

Section: Introductionmentioning

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: Feature Fusion Robustness Experimentsmentioning

confidence: 71%

Section: Layer Namementioning

confidence: 99%

Section: Introductionmentioning

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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“…When compared to the scale-invariant feature transform (SIFT) technique, the proposed method can improve the recognition rate from 86.60% to 93.33%. Vairavel et al [16] presented several methods for extracting features such as the local binary pattern (LBP), histogram of oriented gradients (HOG), and weber local descriptor (WLD), and performance is evaluated in terms of KNN classification accuracy. The WLD method has an accuracy up to 98%, the LBP method has 96% of recognition accuracy, and the HOG method, when compared to both, has the best recognition accuracy up to 99.00%.…”

Section: Related Workmentioning

confidence: 99%

Dorsal hand veins features extraction and recognition by correlation coefficient

Rajab

Hashim

2022

TELKOMNIKA

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One of the most convenient biometrics approaches for identifying a person is dorsal hand veins recognition. In recent years, the dorsal hand veins have acquired increasing attention because of its characteristics such as universal, unique, permanent, contactless, and difficulty of forging, also, the veins remain unchanged when a human being grows. The captured dorsal hand veins image suffers from the many differences in lighting conditions, brightness, existing hair, and amount of noise. To solve these problems, this paper aims to extract and recognize dorsal hand veins based on the largest correlation coefficient. The proposed system consists of three stages: 1) preprocessing the image, 2) feature extraction, and 3) matching. In order to evaluate the proposed system performance, two databases have been employed. The test results illustrate the correct recognition rate (CRR), and accuracy of the first database are 99.38% and 99.46%, respectively, whereas the CRR, and accuracy of the second database are 99.11% and 99.07% respectively. As a result, we conclude that our proposed method for recognizing dorsal hand veins is feasible and effective.

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An Efficient Model for Dorsal Hand Vein Recognition Based on Combination of Squeeze-and-Excitation Block and Vanilla ResNet

Zong,

Bai,

Liu

et al. 2023

Proceedings of 2nd International Conference on Artificial Intelligence, Robotics, and Communication

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Performance analysis on feature extraction using dorsal hand vein image

Cited by 6 publications

References 7 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

Dorsal hand veins features extraction and recognition by correlation coefficient

An Efficient Model for Dorsal Hand Vein Recognition Based on Combination of Squeeze-and-Excitation Block and Vanilla ResNet

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