Emotion and Facial Expression

Wehrle, Thomas; Kaiser, Stephan

doi:10.1007/10720296_5

Cited by 25 publications

(18 citation statements)

References 18 publications

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“…It combines appraisal theory principles and coping predictions in order to model behavior and emotional states. Other modeling attempts also adopt appraisal theory principles and test them empirically in data collection environments such as games or simple interaction interfaces [16,17].…”

Section: Discussionmentioning

confidence: 99%

Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis

et al. 2007

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Appraisal theories in psychology study facial expressions in order to deduct information regarding the underlying emotion elicitation processes. Scherer's component process model provides predictions regarding particular face muscle deformations that are attributed as reactions to the cognitive appraisal stimuli in the study of emotion episodes. In the current work, MPEG-4 facial animation parameters are used in order to evaluate these theoretical predictions for intermediate and final expressions of a given emotion episode. We manipulate parameters such as intensity and temporal evolution of synthesized facial expressions. In emotion episodes originating from identical stimuli, by varying the cognitive appraisals of the stimuli and mapping them to different expression intensities and timings, various behavioral patterns can be generated and thus different agent character profiles can be defined. The results of the synthesis process are consequently applied to Embodied Conversational Agents (ECAs), aiming to render their interaction with humans, or other ECAs, more affective.

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Section: Discussionmentioning

confidence: 99%

Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis

et al. 2007

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“…Though seminal work by Ekman (1989Ekman ( , 1993 and Ekman and Friesen (1971) has shown that humans are universally adept at labeling the emotions conveyed by unambiguous expressions, such displays are fairly rare in everyday social interaction (Wehrle & Kaiser, 2000). Consequently, despite this facility for decoding unambiguous expressions, understanding factors that influence the perception of ambiguous facial expressions is of increasing interest.…”

Section: Perceiving Ambiguous Facial Affectmentioning

confidence: 98%

Familiar eyes are smiling: on the role of familiarity in the perception of facial affect

Claypool

Hugenberg

Housley

et al. 2007

Euro J Social Psych

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Quickly and accurately perceiving others' facial affect is paramount for successful social interaction. This work investigates the role of familiarity in helping us to interpret others' facial emotions. In Experiments 1 and 2, participants viewed several faces, some familiar and some novel, and judged how happy each face appeared. As predicted, results showed that familiar faces were perceived as happier than were novel faces. In Experiment 3, participants again viewed several faces, some familiar and some not, and rated the perceived anger or happiness of these faces. As expected, familiar faces were perceived as happier and less angry than were novel faces. Thus, these results suggest that familiarity is one cue we use to interpret the facial affect of others.

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“…For example, "true smiles" can occur with negative experiences [18]. The Facial Action Coding System (FACS) [4,21] is a catalogue of unique action units (AUs) that correspond to each independent motion of the face. FACS enables the measurement and scoring of facial activity in an objective, reliable and quantitative way, and is often used to discriminate between subtle differences in facial motion.…”

Section: Introductionmentioning

confidence: 99%

Affect valence inference from facial action unit spectrograms

McDuff

Kaliouby

Picard

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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The face provides an important channel for communicating affect valence, the positive or negative emotional charge of an experience. This paper addresses the challenging pattern recognition problem of assigning affect valence labels (positive, negative or neutral) to facial action sequences obtained from unsegmented videos coded using the Facial Action Coding System (FACS). The data were obtained from viewers watching eight short movies with each second of video labeled with self-reported valence and hand coded using FACS. We identify the most frequently occurring Facial Actions and propose the usefulness of a Facial Action Unit Spectrogram. We compare both generative and discriminative classifiers on accuracy and computational complexity: Support Vector Machines, Hidden Markov Models, Conditional Random Fields and Latent-Dynamic Conditional Random Fields. We conduct three tests of generalization with each model. The results provide a first benchmark for classification of self-report valences from spontaneous expressions from a large group of people (n=42). Success is demonstrated for increasing levels of generalization and discriminative classifiers are shown to significantly outperform generative classifiers over this large data set. We discuss the challenges encountered in dealing with a naturalistic dataset with sparse observations and its implications on the results.

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Emotion and Facial Expression

Cited by 25 publications

References 18 publications

Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis

Towards modeling embodied conversational agent character profiles using appraisal theory predictions in expression synthesis

Familiar eyes are smiling: on the role of familiarity in the perception of facial affect

Affect valence inference from facial action unit spectrograms

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