Comparing, Differentiating, and Applying Affective Facial Coding Techniques for the Assessment of Positive Emotion

ABSTRACT

Coding of facial expressions is a simple, fast, inexpensive, and straightforward method to complement self-report research. This approach is particularly relevant for assessing psychological factors such as positive emotion that may be particularly susceptible to self-report biases. The goals of this paper are to 1) provide convincing evidence that this well-validated method should be used in emotion research and 2) provide information about specific facial coding techniques to investigators who are looking to incorporate these methods into their research. We discuss the value, strengths, weaknesses, and applications of four techniques: facial electromyography (EMG), the Facial Action Coding System (FACS), computerized approaches, and modified/simplified hand coding techniques. Taken together, this paper provides a foundation from which researchers can integrate comprehensive and objective assessments of positive emotion into their investigations of human behavior and lays the groundwork for future research to build upon these facial analysis techniques.

KEYWORDS:

• Facial expression
• smiling
• electromyography
• positive emotion
• facial action coding system

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. See recent chapter by Sauter and Russell (2020) for a thorough review of evidence on ties between emotion and facial expression, but also the possibility of factors like misinformation, communication goals, and appraisals being conveyed through the face.

2. There are a number of other facial recognition software programs. One popular program, AFFDEX, was developed by Affectiva and is typically used in market research to assess behavioral responses (Affectiva, 2020; Dupré et al., 2020; iMotions, n.d.; Stöckli et al., 2017). Cognitive Services: Face was developed by Microsoft (accessible through Microsoft Azure) and allows for facial recognition through mobile applications. It also has the unique ability to detect facial expressions in multiple people at the same time (Dupré et al., 2020; Microsoft Azure, n.d.). EmotionalTracking from MorphCast was designed to help adapt content for viewers depending on facial response (Dupré et al., 2020; MorphCast, 2020). FaceAnalysis was developed by Visage Technologies and is used for facial recognition and identification verification (Dupré et al., 2020; Visage Technologies, n.d.). Crowd Emotion developed FaceVideo to recognize emotion through video, and its primary use is for business and media-related research (Crowd Emotion, n.d. 2020; Dupré et al., 2020). Vision AI is an open source facial recognition cloud-based program available through Google (n.d.) and has the ability to recognize four emotions: anger, joy, sorrow, and surprise (Google, n.d., 2020a, 2020b, 2020c; Khanal et al., 2018).

Additional information

Funding

This work was supported by the AXA Research Fund