Biometric Recognition Based on Hand Electromagnetic Scattering at Microwaves

• Published in: IEEE transactions on microwave theory and techniques Vol. 71; no. 11; pp. 1 - 13
• Main Authors: Maiorana, Emanuele, Ramaccia, Davide, Stefanini, Luca, Toscano, Alessandro, Bilotti, Filiberto, Campisi, Patrizio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biometric recognition; Biometrics; Biometrics (access control); Body parts; Character recognition; electromagnetic (EM) metamaterials; Electromagnetic scattering; Fingerprint recognition; hand biometrics; microwave (MW) antennas; Microwave theory and techniques; Microwaves; passive circuits; Recognition; S parameters; Scattering parameters; signal processing; Veins; Wrist
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2023.3300175

In recent years, the need for secure, reliable, and easy-to-use automatic recognition approaches has favored the diffusion of biometric systems. Such methods exploit individuals' physical or behavioral characteristics to differentiate between legitimate users and potential impostors. In this article, we evaluate the feasibility of recognizing a subject by exploiting the electromagnetic (EM) scattering from the user's hand or wrist when it interacts with an EM field at microwave (MW) frequencies. In detail, the proposed recognition framework requires a subject to place a hand between two antennas emitting at frequencies in the X -band, i.e., between 8.2 and 12.4 GHz. The measured scattering parameters are employed as discriminative characteristics to perform user recognition. To improve the achievable recognition performance, an EM model of the involved body parts has been studied and used to design a metasurface (MTS) wristband that could emphasize subject-specific characteristics. The effectiveness of the proposed approach has been tested by collecting multiple measurements from 43 subjects during two distinct acquisition sessions. The obtained results testify that the proposed approach can be used either to implement a robust stand-alone biometric system or exploited together with other well-established hand characteristics such as fingerprint, palm print, or hand vein to improve the achievable recognition performance.