Scanning the Voice of Your Fingerprint With Everyday Surfaces

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kang et al., 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, voice of fingerprint) from a user swiping his fingertip on everyday surfaces. Specifically, we develop SonicPrint to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. SonicPrint is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy.

Automated summary

This paper introduces a new fingerprint sensing technology called SonicPrint, which utilizes the intrinsic fingerprint ridge information in sonic wave for user identification. It is a practical technology that requires no hardware modifications and leverages the built-in microphones in smart devices. The experimental results show a high accuracy rate.