Abstract
The tilt of the closed palm can make it impossible to accurately locate the region of interest (ROI) in contactless palmprint recognition systems. In order to accurately extract the closed palm ROI in the tilted stance, firstly, this paper combines the skeleton point detection and perspective transformation techniques for the correction of tilted images, and then adopts DeepLabv3+ network to eliminate complex backgrounds and combines Gaussian skin colour model to improve segmentation accuracy. Convex packet detection is used to obtain the hand depression points, and combined with horizontal edge detection to filter out the final valley points. Experiments on the open-source dataset 11k Hands and the homemade dataset HUTB_Hands prove that the extraction success rate of the proposed method reaches more than 95%, which is superior to other methods under the same conditions.
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Acknowledgements
This research received partial support from the National Natural Science Foundation of China (61976088) and Research Foundation of Education Bureau of Hunan Province, China (22A0440). The authors would like to extend their sincere gratitude to York University, Assiut University and scholars for their generous provision of the palmprint dataset and other publicly available information.
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Zhou, K., Sun, X., Zhou, X., Zeng, Q. (2023). Region of Interest Extraction for Closed Palm with Complex Background. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_4
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DOI: https://doi.org/10.1007/978-981-99-8565-4_4
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