The effect of dynamics on identifying basic emotions from synthetic and natural faces

Kätsyri, Jari; Sams, Mikko

doi:10.1016/j.ijhcs.2007.10.001

Cited by 86 publications

(70 citation statements)

References 39 publications

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“…For example, some studies reported that dynamic stimuli offer processing advantages (e.g., Ambadar, Schooler, & Cohn, 2005;Bould & Morris, 2008;Cunningham & Wallraven, 2009;Wehrle et al, 2000), namely due to the salience of emotional expressions during dynamic stimuli exposure (e.g., Horstmann & Ansorge, 2009;Rubenstein, 2005). Other studies suggested the contrary (e.g., Fiorentini & Viviani, 2011;Katsyri & Sams, 2008). Yet others indicated no difference between the effects of different stimulus presentation formats.…”

Section: Static Versus Dynamic Facial Expressionsmentioning

confidence: 99%

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

et al. 2016

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This article presents subjective rating norms for a new set of Stills And Videos of facial Expressions-the SAVE database. Twenty nonprofessional models were filmed while posing in three different facial expressions (smile, neutral, and frown). After each pose, the models completed the PANAS questionnaire, and reported more positive affect after smiling and more negative affect after frowning. From the shooting material, stills and 5 s and 10 s videos were edited (total stimulus set = 180). A different sample of 120 participants evaluated the stimuli for attractiveness, arousal, clarity, genuineness, familiarity, intensity, valence, and similarity. Overall, facial expression had a main effect in all of the evaluated dimensions, with smiling models obtaining the highest ratings. Frowning expressions were perceived as being more arousing, clearer, and more intense, but also as more negative than neutral expressions. Stimulus presentation format only influenced the ratings of attractiveness, familiarity, genuineness, and intensity. The attractiveness and familiarity ratings increased with longer exposure times, whereas genuineness decreased. The ratings in the several dimensions were correlated. The subjective norms of facial stimuli presented in this article have potential applications to the work of researchers in several research domains. From our database, researchers may choose the most adequate stimulus presentation format for a particular experiment, select and manipulate the dimensions of interest, and control for the remaining dimensions. The full stimulus set and descriptive results (means, standard deviations, and confidence intervals) for each stimulus per dimension are provided as supplementary material.

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Section: Static Versus Dynamic Facial Expressionsmentioning

confidence: 99%

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

et al. 2016

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“…They used the intensity information to categorize emotional faces into the basic classes. Katsyri and Sams [18] proposed to use dynamic facial expressions as a more accurate detector of emotions. Isbister et al [16] used the continuous model to evaluate emotion from facial expressions.…”

Section: Emotion Detection From Facial Expressionmentioning

confidence: 99%

Towards affective touch interaction: predicting mobile user emotion from finger strokes

2015

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The role of affect and emotion in interactive system design is an active and recent research area. The aim is to make systems more responsive to user's needs and expectations. The first step towards affective interaction is to recognize user's emotional state. Literature contains many works on emotion recognition. In those works, facial muscle movement, gestures, postures and physiological signals were used for recognition. The methods are computation intensive and require extra hardware (e.g., sensors and wires). In this work, we propose a simpler model to predict the affective state of a touch screen user. The prediction is done based on the user's touch input, namely the finger strokes. We defined seven features based on the strokes. A linear combination of these features is proposed as the predictor, which can predict a user's affective state into one of the three states: positive (happy, excited and elated), negative (sad, anger, fear, disgust) and neutral (calm, relaxed and contented). The model alleviates the need for extra setup as well as extensive computation, making it suitable for implementation on mobile devices with limited resources. The model is developed and validated with empirical data involving 57 participants performing 7 touch input tasks. The validation study demonstrates a high prediction accuracy of 90.47 %. The proposed model and its empirical development and validation are described in this paper.

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“…Details of stimulus preparation and evaluation can be found in Kätsyri (2006) and Kätsyri and Sams (2008). In short, the TKK dynamic facial stimuli were carefully designed to portray facial expressions as naturally as possible.…”

Section: Facial Expression Stimulimentioning

confidence: 99%

Valence Scaling of Dynamic Facial Expressions is Altered in High-Functioning Subjects with Autism Spectrum Disorders: an fMRI Study

Rahko

Paakki

Starck

et al. 2011

J Autism Dev Disord

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FMRI was performed with the dynamic facial expressions fear and happiness. This was done to detect differences in valence processing between 25 subjects with autism spectrum disorders (ASDs) and 27 typically developing controls. Valence scaling was abnormal in ASDs. Positive valence induces lower deactivation and abnormally strong activity in ASD in multiple regions. Negative valence increased deactivation in visual areas in subjects with ASDs. The most marked differences between valences focus on fronto-insular and temporal regions. This supports the idea that subjects with ASDs may have difficulty in passive processing of the salience and mirroring of expressions. When the valence scaling of brain activity fails, in contrast to controls, these areas activate and/or deactivate inappropriately during facial stimuli presented dynamically.

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The effect of dynamics on identifying basic emotions from synthetic and natural faces

Cited by 86 publications

References 39 publications

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

Towards affective touch interaction: predicting mobile user emotion from finger strokes

Valence Scaling of Dynamic Facial Expressions is Altered in High-Functioning Subjects with Autism Spectrum Disorders: an fMRI Study

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