Статья

The Effect of Pre-processing on a Convolutional Neural Network Model for Dorsal Hand Vein Recognition

Omar TarawnehDepartment of Computer Science-Faculty of Information Technology, Zarqa University, Zarqa 13100, Jordan 2,Qotadeh SaberSoftware Engineering Department, Amman Arab University, Amman, JordanAhmed AlmaghthawiDepartment of Medical Engineering-Faculty of Engineering, Al-Ahliyya Amman University, 19328, Amman, JordanHamza Abu OwidaFaculty of Information Technology, Applied Science Private University, Amman, JordanAbedalhakeem IssaSoftware Engineering Department, Amman Arab University, Amman, JordanNawaf Farhan Funkur AlshdaifatComputer Science and Computer, Information System Departments, Amman Arab University, Amman, JordanGhaith M. JaradatSoftware Engineering Department, Amman Arab University, Amman, JordanSuhaila AbuowaidaSoftware Engineering Department, Amman Arab University, Amman, JordanMohammad ArabiatDepartment of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi Arabia

2024en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.14569/ijacsa.2024.01503126

Цитирования и источники

Цитирований: 7Использованных источников: 0

Показатели — AkademScholar