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Adapting Psychologically Grounded Facial Emotional Expressions to Different Anthropomorphic Embodiement Platforms
- In Proceedings of the International Conference Florida on Artificial Intelligence
, 2007
"... Starting from the assumptions that human-ambient in-telligence interaction will be improved by having more human-human like communications and that facial ex-pressions are fundamental in human-human communi-cations, we describe how we developed facial expres-sions for artificial agents based on a ps ..."
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Starting from the assumptions that human-ambient in-telligence interaction will be improved by having more human-human like communications and that facial ex-pressions are fundamental in human-human communi-cations, we describe how we developed facial expres-sions for artificial agents based on a psychological the-ory developed by Scherer. In this current article we describe briefly the psycholog-ical theory that we have chosen as well as some of the reason for adopting it. We then describe the two differ-ent platforms we used, the Cherry avatar and the iCat Robot with a particular focus on the robot. Finally we explore the steps needed to adapt the psychological the-ory to the two different platforms and we present some conclusions and future development.
Semantic audio-visual data fusion for automatic emotion recognition
- In Euromedia 2008
, 2008
"... Machines. The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal s ..."
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Machines. The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.
11 Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept
"... www.his.sunderland.ac.uk Abstract. Information processing and responding to sensory input with appropriate actions are among the most important capabilities of the brain and the brain has specific areas that deal with auditory or visual processing. The auditory information is sent first to the cochl ..."
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www.his.sunderland.ac.uk Abstract. Information processing and responding to sensory input with appropriate actions are among the most important capabilities of the brain and the brain has specific areas that deal with auditory or visual processing. The auditory information is sent first to the cochlea, then to the inferior colliculus area and then later to the auditory cortex where it is further processed so that then eyes, head or both can be turned towards an object or location in response. The visual information is processed in the retina, various subsequent nuclei and then the visual cortex before again actions will be performed. However, how is this information integrated and what is the effect of auditory and visual stimuli arriving at the same time or at different times? Which information is processed when and what are the responses for multimodal stimuli? Multimodal integration is first performed in the Superior Colliculus, located in a subcortical part of the midbrain. In this chapter we will focus on this first level of multimodal integration, outline various approaches of modelling the superior colliculus, and suggest a model of multimodal integration of visual and auditory information. 1
TOWARDS AN ARCHITECTURE MODEL FOR EMOTION RECOGNITION IN INTERACTIVE SYSTEMS: APPLICATION TO A BALLET DANCE SHOW
, 2014
"... In the context of the very dynamic and challenging domain of affective computing, we adopt a software engineering point of view on emotion recognition in interactive systems. Our goal is threefold: first, developing an architecture model for emotion recognition. This architecture model emphasizes mu ..."
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In the context of the very dynamic and challenging domain of affective computing, we adopt a software engineering point of view on emotion recognition in interactive systems. Our goal is threefold: first, developing an architecture model for emotion recognition. This architecture model emphasizes multimodality and reusability. Second, developing a prototype based on this architecture model. For this prototype we focus on gesturebased emotion recognition. And third, using this prototype for augmenting a ballet dance show. We hence describe an overview of our work so far, from the design of a flexible and multimodal emotion recognition architecture model, to a presentation of a gesture-based emotion recognition prototype based on this model, to a prototype that augments a ballet stage, taking emotions as inputs.
Multimodal Affect Recognition in Intelligent Tutoring Systems
"... Abstract. This paper concerns the multimodal inference of complex mental states in the intelligent tutoring domain. The research aim is to provide intervention strategies in response to a detected mental state, with the goal being to keep the student in a positive affect realm to maximize learning p ..."
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Abstract. This paper concerns the multimodal inference of complex mental states in the intelligent tutoring domain. The research aim is to provide intervention strategies in response to a detected mental state, with the goal being to keep the student in a positive affect realm to maximize learning potential. The research follows an ethnographic approach in the determination of affective states that naturally occur between students and computers. The multimodal inference component will be evaluated from video and audio recordings taken during classroom sessions. Further experiments will be conducted to evaluate the affect component and educational impact of the intelligent tutor.
9.55 An Interface to Simplify Annotation of Emotional Behaviour
"... 10.05- 10.45 Coffee break (pose posters) Acted versus spontaneous emotions (chairman R. Cowie) 10.45 Anger detection performances based on prosodic and acoustic cues in several corpora Laurence Vidrascu, Laurence Devillers, LIMSI-CNRS, France 11.05 Recording audio-visual emotional databases from act ..."
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10.05- 10.45 Coffee break (pose posters) Acted versus spontaneous emotions (chairman R. Cowie) 10.45 Anger detection performances based on prosodic and acoustic cues in several corpora Laurence Vidrascu, Laurence Devillers, LIMSI-CNRS, France 11.05 Recording audio-visual emotional databases from actors: a
>> IEEE TRANSACTIONS ON MULTIMEDIA, SPECIAL ISSUE ON MULTIMODAL AFFECTIVE INTERACTION < 1
"... Abstract — Many approaches to facial expression recognition focus on assessing the six basic emotions (anger, disgust, happiness, fear, sadness, and surprise). Real-life situations proved to produce many more subtle facial expressions. A reliable way of analyzing the facial behavior is the Facial Ac ..."
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Abstract — Many approaches to facial expression recognition focus on assessing the six basic emotions (anger, disgust, happiness, fear, sadness, and surprise). Real-life situations proved to produce many more subtle facial expressions. A reliable way of analyzing the facial behavior is the Facial Action Coding System (FACS) developed by Ekman and Friesen, which decomposes the face into 46 action units (AU) and is usually performed by a human observer. Each AU is related to the contraction of one or more specific facial muscles. In this study we present an approach towards automatic AU recognition enabling recognition of an extensive palette of facial expressions. As distinctive features we used motion flow estimators between every two consecutive frames, calculated in special regions of interest (ROI). Even though a lot has been published on the facial expression recognition theme, it is still difficult to draw a conclusion regarding the best methodology as there is no common basis for comparison. Therefore our main contributions reside in the comparison of different ROI selections proposed by us, different optical flow estimation methods, and also in the comparison of two spatial-temporal classification methods: Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN). The classifiers have been trained and tested on the Cohn-Kanade database. The experiments showed that under the same conditions regarding initialization, labeling and sampling, both methods produced similar results, achieving the same recognition rate of 89 % for the classification of facial AUs. Still, by enabling non-fixed sampling and using HTK, HMMs rendered a better performance of 93 % suggesting that are better suited for the special task of AUs recognition.
