Abstract
AbstractIn this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN). Firstly, implementation of raw images the region of interest (ROI) of dorsal hand vein images was extracted, and then contrast limited adaptive histogram equalization (CLAHE) and were used to preprocess the images. Next, the extraction of information using the Sato filter and the Otsu thresholding algorithm to create a new database containing only the processed images. Finally, CNN was applied for identification. The experimental results was has been optimized with Hyperparameter Optimization. The dorsal hand vein recognition rate reaches 99%.KeywordsDorsal hand vein recognitionDeep learningConvolutional neural networkCLAHESATOOTSUMask
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
