Abstract
Human faces express emotions, informing others about their affective states. In order to measure expressions of emotion, facial Electromyography (EMG) has widely been used, requiring electrodes and technical equipment. More recently, emotion recognition software has been developed that detects emotions from video recordings of human faces. However, its validity and comparability to EMG measures is unclear. The aim of the current study was to compare the Affectiva Affdex emotion recognition software by iMotions with EMG measurements of the zygomaticus mayor and corrugator supercilii muscle, concerning its ability to identify happy, angry and neutral faces. Twenty participants imitated these facial expressions while videos and EMG were recorded. Happy and angry expressions were detected by both the software and by EMG above chance, while neutral expressions were more often falsely identified as negative by EMG compared to the software. Overall, EMG and software values correlated highly. In conclusion, Affectiva Affdex software can identify facial expressions and its results are comparable to EMG findings.
Highlights
Identifying which emotion somebody is expressing is a crucial skill, facilitating social interactions
The current study investigated whether the Affectiva software can identify different facial expressions and the emotions related to them as efficiently as EMG, by testing participants once with EMG and once with a video recording in separate sessions, with the latter analyzed off-line with Affectiva software
−1 and 1, ensuring the comparability of EMG and Affectiva measures. The computation of these scores resulted in one value for the EMG measure, one value for the Affectiva measure of emotion and one value for the Affectiva measure of expression
Summary
Identifying which emotion somebody is expressing is a crucial skill, facilitating social interactions. Different theories have been built, ranging from the view that many different emotional expressions are possible Scherer, 1999; Scherer et al, 2001; Ellsworth and Scherer, 2003) to the view that a limited number of distinct and basic categories of facial emotion expressions can be distinguished (Ekman, 1992, 1999). Facial expressions of emotions can be measured using Electromyography (EMG, Fridlund and Cacioppo, 1986; Van Boxtel, 2010). EMG records muscle activity using electrodes placed on the skin surface. Distinct muscle activity is observable in response to different emotions. The zygomaticus mayor muscle reliably becomes active during expressions of happiness or more generally joy (smiles). The corrugator supercilii muscle – the muscle that draws the eyebrow downward and medially and produces frowning – is related to angry facial expressions (Dimberg, 1982; Tan et al, 2012), to negative non-facial stimuli (e.g. Bayer et al, 2010; Künecke et al, 2015) as well as cognitive
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