The eyes have it: Making positive expressions more positive and negative expressions more negative.

Messinger, Daniel S.; Mattson, Whitney I.; Mahoor, Mohammad H.; Cohn, Jeffrey F.

doi:10.1037/a0026498

Cited by 125 publications

(92 citation statements)

References 53 publications

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“…For 88 percent of the smile data they collect, the appearance of AU12 was either simultaneously with or closely followed by one or more associated AUs, and for these smiles with multiple AUs, AU6 was the first AU to follow AU12 in 47 percent. Messinger et al [45] also show that AU6 may follow AU12 (smile) or AU20 (cry) to act as an enhancer to enhance the emotion. This means that certain AU in next time step may be affected by other AUs in the current time step.…”

Section: Constraints On Au Dynamicsmentioning

confidence: 99%

Data-Free Prior Model for Facial Action Unit Recognition

Chen

Zhao

et al. 2013

IEEE Trans. Affective Comput.

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Abstract-Facial action recognition is concerned with recognizing the local facial motions from image or video. In recent years, besides the development of facial feature extraction techniques and classification techniques, prior models have been introduced to capture the dynamic and semantic relationships among facial action units. Previous works have shown that combining the prior models with the image measurements can yield improved performance in AU recognition. Most of these prior models, however, are learned from data, and their performance hence largely depends on both the quality and quantity of the training data. These data-trained prior models cannot generalize well to new databases, where the learned AU relationships are not present. To alleviate this problem, we propose a knowledge-driven prior model for AU recognition, which is learned exclusively from the generic domain knowledge that governs AU behaviors, and no training data are used. Experimental results show that, with no training data but generic domain knowledge, the proposed knowledge-driven model achieves comparable results to the data-driven model for specific database and significantly outperforms the data-driven models when generalizing to new data set.

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Section: Constraints On Au Dynamicsmentioning

confidence: 99%

Data-Free Prior Model for Facial Action Unit Recognition

Chen

Zhao

et al. 2013

IEEE Trans. Affective Comput.

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“…Comparison of confusion matrices suggests that fear and surprise are most likely to be confused for the other in both supervisor and secretary conditions, whereas sadness, anger, and disgust are most often confused with each other, with anger more recognizable in both conditions. This might be due to automatic fixation on the eyes, which in surprise and fear tend to be wide open, whereas in anger and disgust the eyes are narrowed and focused (Messinger et al 2011).…”

Section: Discussionmentioning

confidence: 98%

“…Happiness-reassurance displays consist of various types of smiles that are distinguished by various degrees and shapes of mouth opening, typically through the lip pull-up and at an angle by the zygomaticus muscle (Mortillaro, Mehu, and Scherer 2010;Stewart and Ford Dowe 2013), as well as by the co-activation of the orbicularis oculi, a ring of muscles surrounding the eye which raise the cheeks and produce crow's-feet wrinkles when stimulated (Ekman and Friesen 1982;Messinger et al 2011). Smiles influence social interactions and relationships by signaling non-aggressive intentions, with recent research suggesting that different types of smiles play different roles in social interactions (Mehu and Dunbar 2008).…”

Section: Happiness-reassurancementioning

confidence: 99%

Sex and Leadership: Interpreting Competitive and Affiliative Facial Displays Based on Workplace Status

Stewart

Méhu

Salter³

2015

International Public Management Journal

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Increasingly there is an appreciation for face-to-face relationships in workplace interactions, and concomitantly emotion encoding and decoding. However, little research has considered how facial displays of emotion are interpreted based upon the poser's workplace status and the influence of their sex. To consider this, this article first reviews literature pertaining to facial displays of emotion and their function in supervisor-subordinate workplace interactions as well as expectations for men and women. We then test these expectations through an experiment asking respondents to identify the emotion posed by individuals defined as holding different positions of varying status within a small public sector work unit before concluding by discussing theoretical and workplace implications.

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“…Existing evidence suggests that head movement, facial expression, and attention may be closely coordinated (Michel et al, 1992). With the possible exception of Messinger and colleagues (Messinger et al, 2009(Messinger et al, , 2012, automatic analysis of affect related behavior has only been studied in adults. We investigated whether AAA was sufficiently advanced to reveal the dynamics of emotion communication in infants.…”

mentioning

confidence: 99%

“…Additionally, we wanted to inform subsequent research into how the dynamics of non-verbal behavior in infants may be related to change with development in normative and high-risk samples. Both theory and data suggest that affect communication, manifested by non-verbal behaviors (such as head and face), plays a critical role in infant's social, emotional, and cognitive development (Campos et al, 1983;Tronick, 1989;Izard et al, 2011;Messinger et al, 2012). Differences in how infants respond to positive and negative emotion inductions are predictive of developmental outcomes that include attachment security (Cohn et al, 1991), and behavioral problems (Moore et al, 2001).…”

mentioning

confidence: 99%

Automatic Measurement of Head and Facial Movement for Analysis and Detection of Infants’ Positive and Negative Affect

et al. 2015

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Previous work in automatic affect analysis (AAA) has emphasized static expressions to the neglect of the dynamics of facial movement and considered head movement only a nuisance variable to control. We investigated whether the dynamics of head and facial movements apart from specific facial expressions communicate affect in infants, an under-studied population in AAA. Age-appropriate tasks were used to elicit positive and negative affect in 31 ethnically diverse infants. 3D head and facial movements were tracked from 2D video. Head angles in the horizontal (pitch), vertical (yaw), and lateral (roll) directions were used to measure head movement; and the 3D coordinates of 49 facial points to measure facial movements. Strong effects were found for both head and facial movements. Angular velocity and angular acceleration of head pitch, yaw, and roll were higher during negative relative to positive affect. Amplitude, velocity, and acceleration of facial movement were higher as well during negative relative to positive affect. A linear discriminant analysis using head and facial movement achieved a mean classification rate of positive and negative affect equal to 65% (Kappa = 0.30). Head and facial movements individually and in combination were also strongly related to observer ratings of affect intensity. Our results suggest that the dynamics of head and facial movements communicate affect at ages as young as 13 months. These interdisciplinary findings from behavioral science and computer vision deepen our understanding of communication of affect and provide a basis for studying individual differences in emotion in socio-emotional development.

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The eyes have it: Making positive expressions more positive and negative expressions more negative.

Cited by 125 publications

References 53 publications

Data-Free Prior Model for Facial Action Unit Recognition

Data-Free Prior Model for Facial Action Unit Recognition

Sex and Leadership: Interpreting Competitive and Affiliative Facial Displays Based on Workplace Status

Automatic Measurement of Head and Facial Movement for Analysis and Detection of Infants’ Positive and Negative Affect

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