CNN Approaches for Dorsal Hand Vein Based Identification

Lefkovits, Szidónia; Lefkovits, László; Szilágyi, László

doi:10.24132/csrn.2019.2902.2.7

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…text line segmentation, particularly in non-Latin languages [Has19], is important, the appropriate binarization might be a critical step as well. A similar influence may also be observed in some modern solutions based on the application of deep Convolutional Neural Networks (CNN) for various tasks related to image and video analysis [Lef19].…”

Section: Introductionsupporting

confidence: 56%

Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Michalak¹,

Okarma²

2020

Computer Science Research Notes

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Binarization of document images is one of the most relevant pre-processing operations, leading to a significant decrease of the amount of information used during their further analysis. Since many document images, particularly historical, may be degraded over time, the application of some simple global thresholding methods usually lead to highly unsatisfactory results. A similar situation may occur for unevenly illuminated images, limiting the visibility of various shapes, representing not only the alphanumerical characters. A typical solution of this problem is the application of some adaptive thresholding methods, as well as more sophisticated solutions, proposed recently e.g. during Document Image Binarization Competitions (DIBCO) or TQ-DIB 2019 competition. Nevertheless, due to their relatively high computational demands, there is still a need of some faster methods, leading to high binarization accuracy for challenging benchmark datasets, such as DIBCO or Nabuco. Hence, the adaptation and optimization of the parameters of the fast thresholding method utilizing background estimation, proposed originally for the OCR purposes and verified for unevenly illuminated printed documents, is presented in this paper. The proposed solution has been optimized and verified using the state-of-the-art datasets containing 166 degraded document images together with their ground-truth binary equivalents, leading to better results, also in comparison to much slower adaptive thresholding methods. The performance of all methods used in comparisons has been determined using commonly accepted metrics, such as F-Measure, Accuracy, Distance Reciprocal Distortion (DRD) or Misclassification Penalty Metric (MPM), and relative execution time, calculated for all used image datasets.

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Section: Introductionsupporting

confidence: 56%

Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Michalak¹,

Okarma²

2020

Computer Science Research Notes

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“…This difference was comparatively small with the augmented dataset. To fur- ther our research, we compared our results with the findings of published papers using AlexNet for the same problem [11,13,26]. Some of those studies used a different dataset and employed different strategies than ours in improving the test accuracy.…”

Section: System Performance and Comparison With Existing Methodsmentioning

confidence: 99%

“…Despite the lightweight and simple features of AlexNet, it produced comparable classification accuracy to its deeper counterparts, such as VGG16, ResNet-50, and SqueezeNet [26], suggesting its efficiency in extracting important infor-mation. Thus, AlexNet was used for our experiments.…”

Section: Transfer-learning Alexnetmentioning

confidence: 99%

“…The classification accuracy of CNN models depends upon tuned hyperparameters and the nature of the dataset. Previous studies [2,[11][12][13]26] used CNN techniques to recognize the pattern of DHV. On that note, a study [11] recommended using AlexNet, VGG16, and VGG19 due to their high training accuracy (i.e., 99%), but efforts had yet to be made to test the trained model against unseen data.…”

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confidence: 99%

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Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

et al. 2023

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Objectives: Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem.Methods: Manual segmentation involved selecting a region-of-interest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset.Results: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique.Conclusions: Our technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach.

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“…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). Wang et al [ 6 ] put forward the method of dorsal hand vein recognition based on bit plane and block mutual information.…”

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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CNN Approaches for Dorsal Hand Vein Based Identification

Cited by 9 publications

References 23 publications

Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

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

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