Computerized Facial Emotion Expression Recognition

2019 ◽  
pp. 31-44
Author(s):  
Mattis Geiger ◽  
Oliver Wilhelm
Keyword(s):  
Emotion Expression ◽  
Facial Emotion ◽  
Expression Recognition

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.

Real-time facial expression recognition using smoothed deep neural network ensemble

2020 ◽  
Vol 28 (1) ◽  
pp. 97-111
Author(s):  
Nadir Kamel Benamara ◽  
Mikel Val-Calvo ◽  
Jose Ramón Álvarez-Sánchez ◽  
Alejandro Díaz-Morcillo ◽  
Jose Manuel Ferrández-Vicente ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Facial Emotion Recognition ◽  
Training Data ◽  
Facial Emotion ◽  
Processing Unit ◽  
Expression Recognition ◽  
Neural Network Ensemble

Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.

scholarly journals Psychometric challenges and proposed solutions when scoring facial emotion expression codes

2013 ◽  
Vol 46 (4) ◽  
pp. 992-1006 ◽  
Author(s):  
Sally Olderbak ◽  
Andrea Hildebrandt ◽  
Thomas Pinkpank ◽  
Werner Sommer ◽  
Oliver Wilhelm
Keyword(s):  
Emotion Expression ◽  
Facial Emotion

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.

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