Real-Time Dorsal Hand Recognition Based on Smartphone
Mohamed I. Sayed, Mohamed Taha, Hala H. Zayed · IEEE Access · 2021
The integration of biometric recognition with smartphones becomes necessary for increasing security, especially in financial transactions such as online payment. Vein recognition of dorsal hand is outstanding than other methods like palm, finger, and wrist as it has a wide area to be captured and does not have any wrinkles. Most of the current systems that depend on dorsal hand vein recognition are 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, infrared LEDs, and connected to a smartphone used to collect our dataset. The dataset contains 2200 images collected from both hands of 100 persons. The captured images are processed with light algorithms to improve real-time performance and increase frame rate. The algorithm used for feature detection and extraction is Oriented FAST and Rotated BRIEF (ORB) with K-Nearest Neighbors (K-NN) matching to match features. Another benchmark called Poznan University of Technology (PUT) dataset is used to measure the proposed system efficiency. The result obtained from experimental testing showed that the proposed system had a low Equal Error Rate (EER) with 4.33% and a high frame rate of 29 frames per second (Fps).