Automated and objective action coding of facial expressions in patients with acute facial palsy
- 396 Downloads
Aim of the present observational single center study was to objectively assess facial function in patients with idiopathic facial palsy with a new computer-based system that automatically recognizes action units (AUs) defined by the Facial Action Coding System (FACS). Still photographs using posed facial expressions of 28 healthy subjects and of 299 patients with acute facial palsy were automatically analyzed for bilateral AU expression profiles. All palsies were graded with the House–Brackmann (HB) grading system and with the Stennert Index (SI). Changes of the AU profiles during follow-up were analyzed for 77 patients. The initial HB grading of all patients was 3.3 ± 1.2. SI at rest was 1.86 ± 1.3 and during motion 3.79 ± 4.3. Healthy subjects showed a significant AU asymmetry score of 21 ± 11 % and there was no significant difference to patients (p = 0.128). At initial examination of patients, the number of activated AUs was significantly lower on the paralyzed side than on the healthy side (p < 0.0001). The final examination for patients took place 4 ± 6 months post baseline. The number of activated AUs and the ratio between affected and healthy side increased significantly between baseline and final examination (both p < 0.0001). The asymmetry score decreased between baseline and final examination (p < 0.0001). The number of activated AUs on the healthy side did not change significantly (p = 0.779). Radical rethinking in facial grading is worthwhile: automated FACS delivers fast and objective global and regional data on facial motor function for use in clinical routine and clinical trials.
KeywordsFacial nerve Mimic muscles Asymmetry Facial Action Coding System Grading Palsy
We thank Astrid Wetzel (Media Center, Jena University Hospital) for the photographs of all healthy subjects and patients. We thank Wolfgang H. Miltner (Department of Biological and Clinical Psychology, Friedrich Schiller University Jena) for critical reading of the manuscript.
Conflict of interest
The authors indicate that they have no conflict of interest.
- 5.Stennert E, Fisch U (1977) Facial nerve paralysis scoring system. In: Facial Nerve Surgery. Aesculapius Publishing, ZurichGoogle Scholar
- 16.Ekman P, Friesen WV (1978) Manual of the Facial Action Coding System (FACS). Consulting Psychologists Press, Palo AltoGoogle Scholar
- 20.Haase D, Kemmler M, Guntinas Lichius O et al (2012) Measuring Facial Action Unit Activation Intensities using Active Appearance Models. In: German Association for Pattern Recognition (DAGM) Conference, August 28–31, 2012. Graz, AustriaGoogle Scholar
- 26.Lucey P, Cohn JF, Kanade T et al (2010) The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. Comput Vision Pattern Recog Workshops:94–101Google Scholar
- 28.Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. MIT Press, CambridgeGoogle Scholar
- 33.Hager JC, Ekman P (2005) The asymmetry of facial actions is inconsistent with models of hemispheric specialization. In: Ekman P, Rosenberg EL (eds) What the face reveals. Oxford University Press, OxfordGoogle Scholar
- 34.Volk GF, Karamyan I, Klingner CM et al (2014) Quantitative magnetic resonance imaging volumetry of facial muscles in healthy patients with facial palsy. Plast Reconstruct Surg Glob Open 2(6):e173. doi: 10.1097/GOX.0000000000000128
- 48.Ferrario VF, Sforza C (2007) Anatomy of emotion: a 3D study of facial mimicry. Eur J Histochem EJH 51(Suppl 1):45–52Google Scholar