SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull
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.
Stefan Schneegass, Youssef Oualil, and Andreas Bulling. 2016. SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). Association for Computing Machinery, New York, NY, USA, 1379–1384. DOI:https://doi.org/10.1145/2858036.2858152