Recognition of the finger vascular system using multi‐wavelength imaging

Abstract

There has recently been intensive development of methods for identification and personal verification using the human finger vascular system (FVS). The primary focus of these efforts has been the increasingly sophisticated methods of image processing, and frequently employing machine learning. In this article, we present a new concept of imaging in which the finger vasculature is illuminated using different wavelengths of light, generating multiple FVS images. We hypothesised that the analysis of these image sets, instead of individual images, could increase the effectiveness of identification. Analyses of data from over 100 volunteers, using five different deterministic methods for feature extraction, consistently demonstrated improved identification efficiency with the addition of data obtained from another wavelength. The best results were seen for combinations of diodes between 800 and 900 nm. Finger vascular system observations outside this range were of marginal utility. The knowledge gained from this experiment can be utilised by designers of biometric recognition devices leveraging FVS technology. Our results confirm that developments in this field are not restricted to image processing algorithms, and that hardware innovations remain relevant.

Unmanned Ship Target Recognition System Based on Association Rules

“…(3) Abnormal behavior management module based on rule template The abnormal behavior management module in this paper mainly uses the camera to detect the monitoring area and the association rules to judge the abnormal situation. If the abnormal situation is identified, the alarm will be timely sent and the alarm information will be transmitted to the supervisor for processing [17][18]. The flow chart of intelligent alarm logic is shown in Figure 2.…”

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging

“…Some of the FVS-related publications concern instrumental issues, e.g., the selection of an appropriate NIR length [ 11 , 12 ], the assembly of an appropriate combination of lengths used to improve identification performance [ 13 ], or novel acquisition devices [ 12 , 14 , 15 , 16 , 17 ]. The algorithmic issues at the pre- and post-processing stages are considered as well [ 12 , 18 ].…”