Bodily expressed emotion understanding (BEEU) aims to automatically recognize human emotional expressions from body movements. Psychological research has demonstrated that people often move using specific
motor elements to convey emotions. This work takes three steps to integrate human motor elements to study
BEEU. First, we introduce BoME (body motor elements), a highly precise dataset for human motor elements.
Second, we apply baseline models to estimate these elements on BoME, showing that deep learning methods
are capable of learning effective representations of human movement. Finally, we propose a dual-source solution to enhance the BEEU model with the BoME dataset, which trains with both motor element and emotion
labels and simultaneously produces predictions for both. Through experiments on the BoLD in-the-wild
emotion understanding benchmark, we showcase the significant benefit of our approach. These results may
inspire further research utilizing human motor elements for emotion understanding and mental health analysis.
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Modeling Aesthetics and Emotions in Visual Content: From Vincent van Gogh to Robotics and Vision
As inborn characteristics, humans possess the ability to judge visual aesthetics, feel the emotions from the environment, and comprehend others’ emotional expressions. Many exciting applications become possible if robots or computers can be empowered with similar capabilities. Modeling aesthetics, evoked emotions, and emotional expressions automatically in unconstrained situations, however, is daunting due to the lack of a full understanding of the relationship between low-level visual content and high-level aesthetics or emotional expressions. With the growing availability of data, it is possible to tackle these problems using machine learning and statistical modeling approaches. In the talk, I provide an overview of our research in the last two decades on data-driven analyses of visual artworks and digital visual content for modeling aesthetics and emotions. First, I discuss our analyses of styles in visual artworks. Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh’s brushstrokes are strongly rhythmic. Next, I describe an effort to model the aesthetic and emotional characteristics in visual contents such as photographs. By taking a data-driven approach, using the Internet as the data source, we show that computers can be trained to recognize various characteristics that are highly relevant to aesthetics and emotions. Future computer systems equipped with such capabilities are expected to help millions of users in unimagined ways. Finally, I highlight our research on automated recognition of bodily expression of emotion. We propose a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize the body language of humans. Comprehensive statistical analysis revealed many interesting insights from the dataset. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated.
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- Award ID(s):
- 1921783
- NSF-PAR ID:
- 10295639
- Date Published:
- Journal Name:
- Proceedings of the Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, in conjunction with the ACM International Conference on Multimedia
- Page Range / eLocation ID:
- 15-16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The overall goal of our research is to develop a system of intelligent multimodal affective pedagogical agents that are effective for different types of learners (Adamo et al., 2021). While most of the research on pedagogical agents tends to focus on the cognitive aspects of online learning and instruction, this project explores the less-studied role of affective (or emotional) factors. We aim to design believable animated agents that can convey realistic, natural emotions through speech, facial expressions, and body gestures and that can react to the students’ detected emotional states with emotional intelligence. Within the context of this goal, the specific objective of the work reported in the paper was to examine the extent to which the agents’ facial micro-expressions affect students’ perception of the agents’ emotions and their naturalness. Micro-expressions are very brief facial expressions that occur when a person either deliberately or unconsciously conceals an emotion being felt (Ekman &Friesen, 1969). Our assumption is that if the animated agents display facial micro expressions in addition to macro expressions, they will convey higher expressive richness and naturalness to the viewer, as “the agents can possess two emotional streams, one based on interaction with the viewer and the other based on their own internal state, or situation” (Queiroz et al. 2014, p.2).The work reported in the paper involved two studies with human subjects. The objectives of the first study were to examine whether people can recognize micro-expressions (in isolation) in animated agents, and whether there are differences in recognition based on the agent’s visual style (e.g., stylized versus realistic). The objectives of the second study were to investigate whether people can recognize the animated agents’ micro-expressions when integrated with macro-expressions, the extent to which the presence of micro + macro-expressions affect the perceived expressivity and naturalness of the animated agents, the extent to which exaggerating the micro expressions, e.g. increasing the amplitude of the animated facial displacements affects emotion recognition and perceived agent naturalness and emotional expressivity, and whether there are differences based on the agent’s design characteristics. In the first study, 15 participants watched eight micro-expression animations representing four different emotions (happy, sad, fear, surprised). Four animations featured a stylized agent and four a realistic agent. For each animation, subjects were asked to identify the agent’s emotion conveyed by the micro-expression. In the second study, 234 participants watched three sets of eight animation clips (24 clips in total, 12 clips per agent). Four animations for each agent featured the character performing macro-expressions only, four animations for each agent featured the character performing macro- + micro-expressions without exaggeration, and four animations for each agent featured the agent performing macro + micro-expressions with exaggeration. Participants were asked to recognize the true emotion of the agent and rate the emotional expressivity ad naturalness of the agent in each clip using a 5-point Likert scale. We have collected all the data and completed the statistical analysis. Findings and discussion, implications for research and practice, and suggestions for future work will be reported in the full paper. ReferencesAdamo N., Benes, B., Mayer, R., Lei, X., Meyer, Z., &Lawson, A. (2021). Multimodal Affective Pedagogical Agents for Different Types of Learners. In: Russo D., Ahram T., Karwowski W., Di Bucchianico G., Taiar R. (eds) Intelligent Human Systems Integration 2021. IHSI 2021. Advances in Intelligent Systems and Computing, 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-68017-6_33Ekman, P., &Friesen, W. V. (1969, February). Nonverbal leakage and clues to deception. Psychiatry, 32(1), 88–106. https://doi.org/10.1080/00332747.1969.11023575 Queiroz, R. B., Musse, S. R., &Badler, N. I. (2014). Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces. Presence, 23(2), 191-208. http://dx.doi.org/10.1162/
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null (Ed.)Despite significant vision loss, humans can still recognize various emotional stimuli via a sense of hearing and express diverse emotional responses, which can be sorted into two dimensions, arousal and valence. Yet, many research studies have been focusing on sighted people, leading to lack of knowledge about emotion perception mechanisms of people with visual impairment. This study aims at advancing knowledge of the degree to which people with visual impairment perceive various emotions – high/low arousal and positive/negative emotions. A total of 30 individuals with visual impairment participated in interviews where they listened to stories of people who became visually impaired, encountered and overcame various challenges, and they were instructed to share their emotions. Participants perceived different kinds and intensities of emotions, depending on their demographic variables such as living alone, loneliness, onset of visual impairment, visual acuity, race/ethnicity, and employment status. The advanced knowledge of emotion perceptions in people with visual impairment is anticipated to contribute toward better designing social supports that can adequately accommodate those with visual impairment.more » « less
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