Abstract: There are numerous techniques for identifying users, including cards, passwords, and biometrics. Emerging technologies such as cloud computing, smart gadgets, and home automation have raised users’ awareness of the privacy and security of their data. The current study aimed to utilise the CNN model augmented with various pre-processing filters to create a reliable identification system based on the DHV. In addition, the proposed implementing several pre-processing filters to enhance CNN recognition accuracy. The study used a dataset of 500 hand-vein images extracted from 50 patients, while the dataset training was done using the data augmentation technique. The accuracy of the proposed model in this study in classifying images without using image processing showed that 70% was approved for training. Moreover, the results indicated that using the mean filter to remove the noise gave better results, as the accuracy reached 99% in both training conditions.

Omar Tarawneh, Qotadeh Saber, Ahmed Almaghthawi, Hamza Abu Owida, Abedalhakeem Issa, Nawaf Alshdaifat, Ghaith Jaradat, Suhaila Abuowaida and Mohammad Arabiat, “The Effect of Pre-processing on a Convolutional Neural Network Model for Dorsal Hand Vein Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01503126

@article{Tarawneh2024,
title = {The Effect of Pre-processing on a Convolutional Neural Network Model for Dorsal Hand Vein Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01503126},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01503126},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Omar Tarawneh and Qotadeh Saber and Ahmed Almaghthawi and Hamza Abu Owida and Abedalhakeem Issa and Nawaf Alshdaifat and Ghaith Jaradat and Suhaila Abuowaida and Mohammad Arabiat}
}