Schneegass, Stefan; Oualil, Youssef; Bulling, Andreas

SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI '16, ACM, pp. 1379-1384, New York, NY, USA, 2016, ISBN 978-1-4503-3362-7.

Secure user identification is important for the increasing number of eyewear computers but limited input capabilities pose significant usability challenges for established knowledge-based schemes, such as passwords or PINs. We present SkullConduct, a biometric system that uses bone conduction of sound through the user’s skull as well as a microphone readily integrated into many of these devices, such as Google Glass. At the core of SkullConduct is a method to analyze the characteristic frequency response created by the user’s skull using a combination of Mel Frequency Cepstral Coefficient (MFCC) features as well as a computationally light-weight 1NN classifier. We report on a controlled experiment with 10 participants that shows that this frequency response is person-specific and stable — even when taking off and putting on the device multiple times — and thus serves as a robust biometric. We show that our method can identify users with 97.0% accuracy and authenticate them with an equal error rate of 6.9%, thereby bringing biometric user identification to eyewear computers equipped with bone conduction technology.