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

Waheed Ali Laghari, Waheed Ali Laghari and Audrey Huong, Kim Gaik Tay, Audrey Huong, Kim Gaik Tay and Chang Choon Chew, Chang Choon Chew (2023) Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods. Healthc Inform Research, 29 (2). pp. 152-160. ISSN 2093-3681

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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-ofinterest (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

Item Type: Article
Uncontrolled Keywords: Biometrics, Veins, Classification, Deep Learning, Transfer Learning
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User: Mr. Mohamad Zulkhibri Rahmad
Date Deposited: 13 Sep 2023 07:30
Last Modified: 13 Sep 2023 07:30

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