March 11, 2019

Automatic Smile and Frown Recognition with Kinetic Earables

  • Chang Y.
  • Kawsar F.
  • Lee S.
  • Mathur A.
  • Min C.
  • Montanari A.
  • Song J.

In this paper, we introduce inertial signals obtained from an ear- able placed in the ear canal as a new compelling sensing modality for recognising two key facial expressions: smile and frown. Bor- rowing principles from Facial Action Coding Systems (FACS), we first demonstrate that an inertial measurement unit of an earable can capture facial muscle deformation activated by a set of tem- poral micro-expressions. Building on these observations, we then present three different learning schemes - shallow learning with statistical features, hidden Markov model, and deep neural net- works to accurately recognise smile and frown expressions from inertial signals. The experimental results show that in controlled non-conversational settings, we can identify smile and frown across a diverse user population with high accuracy (F1 score: 0.88).

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, ...

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist ...

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel ...