Reconsidering the Duchenne Smile: Indicator of Positive Emotion or Artifact of Smile Intensity?

Smile variation leaks personality and increases the accuracy of interpersonal judgments

People ubiquitously smile during brief interactions and first encounters, and when posing for photos used for virtual dating, social networking, and professional profiles. Yet not all smiles are the same: subtle individual differences emerge in how people display this nonverbal facial expression. We hypothesized that idiosyncrasies in people's smiles can reveal aspects of their personality and guide the personality judgments made by observers, thus enabling a smiling face to serve as a valuable tool in making more precise inferences about an individual's personality. Study 1 (N = 303) supported the hypothesis that smile variation reveals personality, and identified the facial-muscle activations responsible for this leakage. Study 2 (N = 987) found that observers use the subtle distinctions in smiles to guide their personality judgments, consequently forming slightly more accurate judgments of smiling faces than neutral ones. Smiles thus encode traces of personality traits, which perceivers utilize as valid cues of those traits.

Automated detection of smiles as discrete episodes

BACKGROUND

Patients seeking restorative and orthodontic treatment expect an improvement in their smiles and oral health-related quality of life. Nonetheless, the qualitative and quantitative characteristics of dynamic smiles are yet to be understood.

OBJECTIVE

To develop, validate, and introduce open-access software for automated analysis of smiles in terms of their frequency, genuineness, duration, and intensity.

MATERIALS AND METHODS

A software script was developed using the Facial Action Coding System (FACS) and artificial intelligence to assess activations of (1) cheek raiser, a marker of smile genuineness; (2) lip corner puller, a marker of smile intensity; and (3) perioral lip muscles, a marker of lips apart. Thirty study participants were asked to view a series of amusing videos. A full-face video was recorded using a webcam. The onset and cessation of smile episodes were identified by two examiners trained with FACS coding. A Receiver Operating Characteristic (ROC) curve was then used to assess detection accuracy and optimise thresholding. The videos of participants were then analysed off-line to automatedly assess the features of smiles.

RESULTS

The area under the ROC curve for smile detection was 0.94, with a sensitivity of 82.9% and a specificity of 89.7%. The software correctly identified 90.0% of smile episodes. While watching the amusing videos, study participants smiled 1.6 (±0.8) times per minute.

CONCLUSIONS

Features of smiles such as frequency, duration, genuineness, and intensity can be automatedly assessed with an acceptable level of accuracy. The software can be used to investigate the impact of oral conditions and their rehabilitation on smiles.

Electroencephalographic Signature of Negative Self Perceptions in Medical Students

Frontal alpha asymmetry (fAA) is purported to be a neurophysiological marker for anxiety and depression. Higher left frontal alpha EEG voltage is associated with lower left and higher right frontal cerebral cortical activation, indicative of right-sided fAA. This pilot study tests the hypothesis that greater left-sided frontal alpha voltage is associated with negative thoughts about oneself. A group of eight healthy 28-41-year-old right-handed male medical students were subjected to an extensive interactive self-report inventory (ISI) evaluating perceptions of their psychosocial interactions. Quantitative EEG (qEEG) was performed with eyes closed. Computations of fAA and related parameters were based on measurements in the alpha bandwidth (8-13 Hz) at the left frontal F7 and right frontal F8 scalp electrodes. fAA was the percent difference between mean voltages at F8 minus that at F7. Significance of associations between fAA and the ISI scores was determined by Pearson’s product-moment correlation coefficient, at P≤0.05. “Depressed” scores were positively correlated with right-sided fAA (P=0.01). “Relaxed” (P=0.05), “regulated” (P=0.02), “cooperative” (P=0.05) and “dependent scores” (P=0.004) were negatively correlated with right-sided fAA. These findings imply that right-sided fAA may be associated with more perceptions of “depressed” psychosocial interactions involving negative thoughts about oneself, as well as, more reliance on others (“dependence” score), less sharing (“cooperative” ISI score), less trust (“regulated” ISI score) and less initiative (“relaxed” ISI score). These results support the hypothesis that right-sided fAA may identify individuals with a predilection for negative thoughts about themselves and other negatively-valenced perceptions of their psychosocial interactions.

Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions

Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.

Reconsidering the Duchenne Smile: Formalizing and Testing Hypotheses about Eye Constriction and Positive Emotion

The common view of emotional expressions is that certain configurations of facial-muscle movements reliably reveal certain categories of emotion. The principal exemplar of this view is the Duchenne smile, a configuration of facial-muscle movements (i.e., smiling with eye constriction) that has been argued to reliably reveal genuine positive emotion. In this paper, we formalized a list of hypotheses that have been proposed regarding the Duchenne smile, briefly reviewed the literature weighing on these hypotheses, identified limitations and unanswered questions, and conducted two empirical studies to begin addressing these limitations and answering these questions. Both studies analyzed a database of 751 smiles observed while 136 participants completed experimental tasks designed to elicit amusement, embarrassment, fear, and physical pain. Study 1 focused on participants' self-reported positive emotion and Study 2 focused on how third-party observers would perceive videos of these smiles. Most of the hypotheses that have been proposed about the Duchenne smile were either contradicted by or only weakly supported by our data. Eye constriction did provide some information about experienced positive emotion, but this information was lacking in specificity, already provided by other smile characteristics, and highly dependent on context. Eye constriction provided more information about perceived positive emotion, including some unique information over other smile characteristics, but context was also important here as well. Overall, our results suggest that accurately inferring positive emotion from a smile requires more sophisticated methods than simply looking for the presence/absence (or even the intensity) of eye constriction.