In this paper, the authors explore different approaches to animating 3D facial emotions, some of which use manual keyframe animation and some of which use machine learning. To compare approaches the authors conducted an experiment consisting of side-by-side comparisons of animation clips generated by skeleton, blendshape, audio-driven, and vision-based capture facial animation techniques. Ninety-five participants viewed twenty face animation clips of characters expressing five distinct emotions (anger, sadness, happiness, fear, neutral), which were created using the four different facial animation techniques. After viewing each clip, the participants were asked to identify the emotions that the characters appeared to be conveying and rate their naturalness. Findings showed that the naturalness ratings of the happy emotion produced by the four methods tended to be consistent, whereas the naturalness ratings of the fear emotion created with skeletal animation were significantly higher than the other methods. Recognition of sad and neutral emotions were very low for all methods as compared to the other emotions. Overall, the skeleton approach had significantly higher ratings for naturalness and higher recognition rate than the other methods.
more »
« less
Animated Agents’ Facial Emotions: Does the Agent Design Make a Difference?
The paper reports ongoing research toward the design of multimodal affective pedagogical agents that are effective for different types of learners and applications. In particular, the work reported in the paper investigated the extent to which the type of character design (realistic versus stylized) affects students’ perception of an animated agent’s facial emotions, and whether the effects are moderated by learner characteristics (e.g. gender). Eighty-two participants viewed 10 animation clips featuring a stylized character exhibiting 5 different emotions, e.g. happiness, sadness, fear, surprise and anger (2 clips per emotion), and 10 clips featuring a realistic character portraying the same emotional states. The participants were asked to name the emotions and rate their sincerity, intensity, and typicality. The results indicated that for recognition, participants were slightly more likely to recognize the emotions displayed by the stylized agent, although the difference was not statistically significant. The stylized agent was on average rated significantly higher for facial emotion intensity, whereas the differences in ratings for typicality and sincerity across all emotions were not statistically significant. A significant difference in ratings was shown in regard to sadness (within typicality), happiness (within sincerity), fear, anger, sadness and happiness (within intensity) with the stylized agent rated higher. Gender was not a significant correlate across all emotions or for individual emotions.
more »
« less
- Award ID(s):
- 1821894
- NSF-PAR ID:
- 10152231
- Date Published:
- Journal Name:
- In: De Paolis L., Bourdot P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2019. Lecture Notes in Computer Science, vol 11613. Springer, Cham
- Volume:
- 11613
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
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/
-
In general, people tend to identify the emotions of others from their facial expressions, however recent findings suggest that we may be more accurate when we hear someone’s voice than when we look only at their facial expression. The study reported in the paper examined whether these findings hold true for animated agents. A total of 37 subjects participated in the study: 19 males, 14 females, and 4 of non-specified gender. Subjects were asked to view 18 video stimuli; 9 clips featured a male agent and 9 clips a female agent. Each agent showed 3 different facial expressions (happy, angry, neutral), each one paired with 3 different voice lines spoken in three different tones (happy, angry, neutral). Hence, in some clips the agent’s tone of voice and facial expression were congruent, while in some videos they were not. Subjects answered questions regarding the emotion they believed the agent was feeling and rated the emotion intensity, typicality, and sincerity. Findings showed that emotion recognition rate and ratings of emotion intensity, typicality and sincerity were highest when the agent’s face and voice were congruent. However, when the channels were incongruent, subjects identified the emotion more accurately from the agent’s facial expression than the tone of voice.more » « less
-
Meta-analyses have not shown emotions to be significant predictors of deception. Criticisms of this conclusion argued that individuals must be engaged with each other in higher stake situations for such emotions to manifest, and that these emotions must be evaluated in their verbal context (Frank and Svetieva in J Appl Res Memory Cognit 1:131–133, 10.1016/j.jarmac.2012.04.006, 2012). This study examined behavioral synchrony as a marker of engagement in higher stakes truthful and deceptive interactions, and then compared the differences in facial expressions of fear, contempt, disgust, anger, and sadness not consistent with the verbal content. Forty-eight pairs of participants were randomly assigned to interviewer and interviewee, and the interviewee was assigned to steal either a watch or a ring and to lie about the item they stole, and tell the truth about the other, under conditions of higher stakes of up to $30 rewards for successful deception, and $0 plus having to write a 15-min essay for unsuccessful deception. The interviews were coded for expression of emotions using EMFACS (Friesen and Ekman in EMFACS-7; emotional facial action coding system, 1984). Synchrony was demonstrated by the pairs of participants expressing overlapping instances of happiness (AU6 + 12). A 3 (low, moderate, high synchrony) × 2 (truth, lie) mixed-design ANOVA found that negative facial expressions of emotion were a significant predictor of deception, but only when they were not consistent with the verbal content, in the moderate and high synchrony conditions. This finding is consistent with data and theorizing that shows that with higher stakes, or with higher engagement, emotions can be a predictor of deception.more » « less
-
Facial expressions of emotions by people with visual impairment and blindness via video conferencingmore » « less
Many people including those with visual impairment and blindness take advantage of video conferencing tools to meet people. Video conferencing tools enable them to share facial expressions that are considered as one of the most important aspects of human communication. This study aims to advance knowledge of how those with visual impairment and blindness share their facial expressions of emotions virtually. This study invited a convenience sample of 28 adults with visual impairment and blindness to Zoom video conferencing. The participants were instructed to pose facial expressions of basic human emotions (anger, fear, disgust, happiness, surprise, neutrality, calmness, and sadness), which were video recorded. The facial expressions were analyzed using the Facial Action Coding System (FACS) that encodes the movement of specific facial muscles called Action Units (AUs). This study found that there was a particular set of AUs significantly engaged in expressing each emotion, except for sadness. Individual differences were also found in AUs influenced by the participants’ visual acuity levels and emotional characteristics such as valence and arousal levels. The research findings are anticipated to serve as the foundation of knowledge, contributing to developing emotion-sensing technologies for those with visual impairment and blindness.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1007/978-3-030-25965-5_2
