WristAcoustic: Through-Wrist Acoustic Response Based Authentication for Smartwatches

Abstract

PIN and pattern lock are difficult to accurately enter on small watch screens, and are vulnerable against guessing attacks. To address these problems, this paper proposes a novel implicit biometric scheme based on through-wrist acoustic responses. A cue signal is played on a surface transducer mounted on the dorsal wrist and the acoustic response recorded by a contact microphone on the volar wrist. We build classifiers using these recordings for each of three simple hand poses (relax, fist and open), and use an ensemble approach to make final authentication decisions. In an initial single session study (N=25), we achieve an Equal Error Rate (EER) of 0.01%, substantially outperforming prior on-wrist biometric solutions. A subsequent five recall-session study (N=20) shows reduced performance with 5.06% EER. We attribute this to increased variability in how participants perform hand poses over time. However, after retraining classifiers performance improved substantially, ultimately achieving 0.79% EER. We observed most variability with the relax pose. Consequently, we achieve the most reliable multi-session performance by combining the fist and open poses: 0.51% EER. Further studies elaborate on these basic results. A usability evaluation reveals users experience low workload as well as reporting high SUS scores and fluctuating levels of perceived exertion: moderate during initial enrollment dropping to slight during authentication. A final study examining performance in various poses and in the presence of noise demonstrates the system is robust to such disturbances and likely to work well in wide range of real-world contexts.