FaceInput: A Hand-Free and Secure Text Entry System through Facial Vibration

EasyChair Preprint 1301

Abstract

Wearable wristbands have become prevailing in the recent days because of their small and portable property. However, the limited size of the touch screen causes the problems of fat fingers and screen occlusion. Furthermore, it is not available for users whose hands are fully occupied with other tasks. To break this bottleneck, we propose a portable, hand-free and secure text-entry system,
called FaceInput, which firstly uses a single small form factor sensor to accomplish a practical user input via facial vibrations. To sense the tiny facial vibration signals, we design and implement a double-stage amplifier whose maximum gain is 225. To enhance the input accuracy and robustness, we design a set of novel schemes for FaceInput based on ‘the Mel-frequency cepstral coefficient (MFCC) concept and a hidden Markov model (HMM) to process the vibration signals, and an online calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects during the period of one month. The results demonstrate that FaceInput can be successful to sense the tiny facial vibrations and robust to fight against various confounding factors. The average recognition accuracy is 98.2%. Furthermore, by enabling the runtime calibration and adaptation scheme that updates and enlarges the training data set, the accuracy can reach 100%.

Keyphrases: Text Input, Vibration Recognition, facial expression, wearable devices

@booklet{EasyChair:1301,
  author    = {Maoning Guan and Wenqiang Chen and Yandao Huang and Rukhsana Ruby and Kaishun Wu},
  title     = {FaceInput: A Hand-Free and Secure Text Entry System through Facial Vibration},
  howpublished = {EasyChair Preprint 1301},
  year      = {EasyChair, 2019}}