Open AccessReal-Time Dorsal Hand Recognition Based on Smartphone
Author(s) -
Mohamed I. Sayed,
Mohamed Taha,
Hala H. Zayed
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3126709
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The integration of biometric recognition with smartphones is necessary to increase security, especially in financial transactions such as online payments. Vein recognition of the dorsal hand is superior to other methods such as palm, finger, and wrist, as it has a wide area to be captured and does not have any wrinkles. Most current systems that depend on dorsal hand vein recognition do not work in real-time and have poor results. In this paper, a dorsal hand recognition system working in real-time is proposed to achieve good results with a high frame rate. A contactless device consists of a universal serial bus (USB)camera and infrared LEDs and is connected to a smartphone used to collect our dataset. The dataset contained 2200 images collected from both hands of 100 individuals. The captured images were processed with light algorithms to improve the real-time performance and increase the frame rate. The feature detection and extraction algorithm is oriented FAST and rotated BRIEF (ORB) with K-nearest neighbors (K-NN) matching to match features. Another benchmark, called the Poznan University of Technology (PUT) dataset, is used to measure the efficiency of the proposed system. The results obtained from experimental testing showed that the proposed system had a low equal error rate (EER) of 4.33% and a high frame rate of 29 frames per second.