AcoPalm: Acoustical Palmprint-Based Noncontact Identity Authentication

Biometric sensing has become a widely concerned authentication technology. Existing image-based methods are susceptible to light conditions and have privacy exposure risks, while contact authentication methods are not conducive to epidemic prevention requirements in public places. In this article, we propose a palmprint-based identification system by collecting backscattered signals of the inaudible acoustic signals, namely AcoPalm. AcoPalm does not require special hardware and contact operation for user authentication. First, frequency modulated continuous wave (FMCW) on acoustic signals are designed to extract static contours and palmprint changes and to model the unique biological characteristics of the individual palm. Second, a palmprint authentication model based on PENN is proposed to achieve high-precision multiuser authentication without mass training data. Finally, the system performance is evaluated in multiple smartphones and three scenarios. The experimental results show that AcoPalm can resist replay attack and imitation attack, and the authentication accuracy can reach 96.22%. Furthermore, AcoPalm achieves satisfactory experience in availability and practicality.