Name
Abstract | ||
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In this paper, we introduce inertial signals obtained from an earable placed in the ear canal as a new compelling sensing modality for recognising two key facial expressions: smile and frown. Borrowing principles from Facial Action Coding Systems, we first demonstrate that an inertial measurement unit of an earable can capture facial muscle deformation activated by a set of temporal micro-expressions. Building on these observations, we then present three different learning schemes - shallow models with statistical features, hidden Markov model, and deep neural networks to automatically recognise smile and frown expressions from inertial signals. The experimental results show that in controlled non-conversational settings, we can identify smile and frown with high accuracy (F1 score: 0.85).
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3311823.3311869 | Proceedings of the 10th Augmented Human International Conference 2019 |
Keywords | Field | DocType |
FACS, earable, kinetic modeling, smile and frown recognition | F1 score,Computer vision,Frown,Expression (mathematics),Computer science,Coding (social sciences),Facial muscles,Facial expression,Inertial measurement unit,Artificial intelligence,Hidden Markov model | Conference |
ISBN | Citations | PageRank |
978-1-4503-6547-5 | 2 | 0.36 |
References | Authors | |
7 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seungchul Lee | 1 | 32 | 7.10 |
| Chulhong Min | 2 | 362 | 30.13 |
| Alessandro Montanari | 3 | 7 | 5.02 |
| Akhil Mathur | 4 | 101 | 15.10 |
| Youngjae Chang | 5 | 6 | 3.55 |
| Junehwa Song | 6 | 1384 | 105.08 |
| Fahim Kawsar | 7 | 909 | 80.24 |