Multimodal human emotion/expression recognition

2002 ◽  
Author(s):  
L.S. Chen ◽  
T.S. Huang ◽  
T. Miyasato ◽  
R. Nakatsu
Keyword(s):  
Emotion Expression ◽  
Expression Recognition ◽  
Human Emotion

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 Allocentric Emotional Affordances in HRI: The Multimodal Binding

2018 ◽  
Author(s):  
Jordi Vallverdú ◽  
Gabriele Trovato ◽  
Lorenzo Jamone
Keyword(s):  
Emotion Expression ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Human Emotion ◽  
Important Concept ◽  
Proposed Model ◽  
Arousal Model ◽  
Viable Model

Affordances are an important concept in cognition, which can be applied to robots in order to perform a successful human-robot interaction (HRI). In this paper we explore and discuss the idea of emotional affordances and propose a viable model for implementation into HRI. We consider “2-ways” affordances: perceived object triggering an emotion, and perceived human emotion expression triggering an action. In order to make the implementation generic, the proposed model includes a library that can be customised depending on the specific robot and application’s scenario. We present the AAA (Affordance-Appraisal-Arousal) model, which incorporates Plutchik’s Wheel of Emotions, and show some examples of simulation and possible scenarios.

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 Variational Pathway Reasoning for EEG Emotion Recognition

2020 ◽  
Vol 34 (03) ◽  
pp. 2709-2716
Author(s):  
Tong Zhang ◽  
Zhen Cui ◽  
Chunyan Xu ◽  
Wenming Zheng ◽  
Jian Yang
Keyword(s):  
Random Walk ◽  
Emotion Recognition ◽  
State Of The Art ◽  
Maximum Energy ◽  
Emotion Expression ◽  
Scaling Factors ◽  
Human Emotion ◽  
Generalization Ability ◽  
Probabilistic Process ◽  
Sparsity Constraint

Research on human emotion cognition revealed that connections and pathways exist between spatially-adjacent and functional-related areas during emotion expression (Adolphs 2002a; Bullmore and Sporns 2009). Deeply inspired by this mechanism, we propose a heuristic Variational Pathway Reasoning (VPR) method to deal with EEG-based emotion recognition. We introduce random walk to generate a large number of candidate pathways along electrodes. To encode each pathway, the dynamic sequence model is further used to learn between-electrode dependencies. The encoded pathways around each electrode are aggregated to produce a pseudo maximum-energy pathway, which consists of the most important pair-wise connections. To find those most salient connections, we propose a sparse variational scaling (SVS) module to learn scaling factors of pseudo pathways by using the Bayesian probabilistic process and sparsity constraint, where the former endows good generalization ability while the latter favors adaptive pathway selection. Finally, the salient pathways from those candidates are jointly decided by the pseudo pathways and scaling factors. Extensive experiments on EEG emotion recognition demonstrate that the proposed VPR is superior to those state-of-the-art methods, and could find some interesting pathways w.r.t. different emotions.

Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

2020 ◽  
Vol 34 (14) ◽  
pp. 2056010
Author(s):  
Lei Huang ◽  
Fei Xie ◽  
Jing Zhao ◽  
Shibin Shen ◽  
Weiran Guang ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Human Emotion ◽  
The Face ◽  
Color Enhancement

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

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