Multi-model Emotion Expression Recognition for Lip Synchronization

2020 ◽  
Author(s):  
Salwa A. Al-agha ◽  
Hilal H. Saleh ◽  
Rana F. Ghani
Keyword(s):  
Emotion Expression ◽  
Expression Recognition ◽  
Lip Synchronization

scholarly journals Confidence of emotion expression recognition recruits brain regions outside the face perception network

2018 ◽  
Vol 14 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Indrit Bègue ◽  
Maarten Vaessen ◽  
Jeremy Hofmeister ◽  
Marice Pereira ◽  
Sophie Schwartz ◽  
...  
Keyword(s):  
Face Perception ◽  
Brain Regions ◽  
Emotion Expression ◽  
Expression Recognition ◽  
The Face

Accultured Norms

2021 ◽  
pp. 104-117
Author(s):  
Mari Fitzduff
Keyword(s):  
Gender Differences ◽  
Cultural Differences ◽  
Emotion Expression ◽  
Communication Styles ◽  
Expression Recognition ◽  
The World ◽  
The Many ◽  
Cultural Attunement ◽  
Power And Authority

This chapter looks at the importance of understanding the many cultural differences that exist between different groups and in different contexts around the world. Without a sensitivity to such differences, wars can be lost and positive influences minimized. These differences include the existence of high-context versus low-context societies, differing hierarchical approaches to power and authority, collectivist versus individualist societies, differing emotion expression/recognition, gender differences, differing evidencing of empathy, face preferences, and communication styles. Lack of cultural attunement to these issues can exacerbate misunderstandings and conflicts, unless understood and factored into difficult strategies and dialogues.

scholarly journals Emotion Expression when Teaching with Creative Movement

2020 ◽  
Vol 22 ◽  
Author(s):  
Simona Prosen ◽  
Vesna Geršak ◽  
Helena Smrtnik Vitulić
Keyword(s):  
Teaching Method ◽  
Anger Expression ◽  
Emotion Expression ◽  
Second Grade ◽  
Control Group ◽  
Expression Recognition ◽  
Creative Movement ◽  
Significant Difference ◽  
High Level ◽  
Recognition Software

The study focuses on students emotion expression during geometry teaching including creative movement (experimental group or EG) and without it (control group or CG). The sample (N = 104) was made up of primary school (second-grade) students: 66 were assigned to the EG and 38 to the CG. Of these, 12 students from the EG and 8 from the CG were randomly selected for observation of emotion: type, intensity, triggering situation, and response of others. For the observed students, the intensity of emotion expression was also measured by the facial expression recognition software FaceReader. All of the students self-assessed their contentedness with the teaching. The students in the EG and the CG expressed various emotions, with joy being the most prevalent, followed by anger. The most frequent situations triggering joy were activities in the EG and the CG. The intensity of joy was higher in the EG than in the CG when assessed by observation, but there was no significant difference when assessed by FaceReader. The intensity of anger expression was at a similar level in both groups. Both students and teachers responded to students joy expression, but only the students responded to anger expression in the EG and the CG. The students in both groups expressed a high level of contentedness with the teaching. Key words: creative movement; emotion expression; intensity of emotions; students; teaching method.

scholarly journals Sensitivity to Emotion Intensity and Recognition of Emotion Expression in Neurotypical Children

Children ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 1108
Author(s):  
Koviljka Barisnikov ◽  
Marine Thomasson ◽  
Jennyfer Stutzmann ◽  
Fleur Lejeune
Keyword(s):  
Emotion Recognition ◽  
High Intensity ◽  
Emotion Expression ◽  
Expression Recognition ◽  
Low Intensity ◽  
Face Expression ◽  
Emotion Intensity ◽  
Face Expression Recognition ◽  
Intensity Of Emotion ◽  
Better Than

This study assessed two components of face emotion processing: emotion recognition and sensitivity to intensity of emotion expressions and their relation in children age 4 to 12 (N = 216). Results indicated a slower development in the accurate decoding of low intensity expressions compared to high intensity. Between age 4 and 12, children discriminated high intensity expressions better than low ones. The intensity of expression had a stronger impact on overall face expression recognition. High intensity happiness was better recognized than low intensity up to age 11, while children 4 to 12 had difficulties discriminating between high and low intensity sadness. Our results suggest that sensitivity to low intensity expressions acts as a complementary mediator between age and emotion expression recognition, while this was not the case for the recognition of high intensity expressions. These results could help in the development of specific interventions for populations presenting socio-cognitive and emotion difficulties.

scholarly journals Event-related EEG oscillatory responses elicited by dynamic facial expression

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Tuba Aktürk ◽  
Tom A. de Graaf ◽  
Yasemin Abra ◽  
Sevilay Şahoğlu-Göktaş ◽  
Dilek Özkan ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Face Processing ◽  
Time Window ◽  
Phase Locking ◽  
Time Windows ◽  
Real Life ◽  
Emotion Expression ◽  
Expression Recognition ◽  
Time Frequency ◽  
Recognition Of Facial Expressions

Abstract Background Recognition of facial expressions (FEs) plays a crucial role in social interactions. Most studies on FE recognition use static (image) stimuli, even though real-life FEs are dynamic. FE processing is complex and multifaceted, and its neural correlates remain unclear. Transitioning from static to dynamic FE stimuli might help disentangle the neural oscillatory mechanisms underlying face processing and recognition of emotion expression. To our knowledge, we here present the first time–frequency exploration of oscillatory brain mechanisms underlying the processing of dynamic FEs. Results Videos of joyful, fearful, and neutral dynamic facial expressions were presented to 18 included healthy young adults. We analyzed event-related activity in electroencephalography (EEG) data, focusing on the delta, theta, and alpha-band oscillations. Since the videos involved a transition from neutral to emotional expressions (onset around 500 ms), we identified time windows that might correspond to face perception initially (time window 1; first TW), and emotion expression recognition subsequently (around 1000 ms; second TW). First TW showed increased power and phase-locking values for all frequency bands. In the first TW, power and phase-locking values were higher in the delta and theta bands for emotional FEs as compared to neutral FEs, thus potentially serving as a marker for emotion recognition in dynamic face processing. Conclusions Our time–frequency exploration revealed consistent oscillatory responses to complex, dynamic, ecologically meaningful FE stimuli. We conclude that while dynamic FE processing involves complex network dynamics, dynamic FEs were successfully used to reveal temporally separate oscillation responses related to face processing and subsequently emotion expression recognition.

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