A Practical Guide for Automated Facial Emotion Classification

Stöckli, Sabrina; Schulte-Mecklenbeck, Michael; Borer, Stefan; Samson, Andrea C. (2019). A Practical Guide for Automated Facial Emotion Classification. In: A Handbook of Process Tracing Methods (pp. 198-216). New York: Taylor & Francis

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This chapter discusses a user-oriented introduction of automated facial expression analysis. It focuses on computer-based systems that allow the automated classification of distinct basic emotions. Initially, iMotions implemented automated facial expression analysis based on the FACET algorithm developed by Emotient. Since the implementation of automated facial expression analysis tools, commercial applications of the technology have increased. Automated facial expression analysis is less accurate in measuring subtle, natural facial expressions compared to prototypical facial expressions; however, the accuracy loss is also evident for other methods. The chapter describes a step-by-step overview of applying iMotions Biometric Research Platform for automated facial expression analysis. There are two ways to gain insight into iMotions’s facial expression data: first, iMotions interface transfers respondent-wise raw data into live graphs showing the probability of the presence of all basic emotions and valence. Second, iMotions allows the export of comprehensive raw data.

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