Abstract: The human face in particular serves not only communicative functions, but it is also the primary channel to express emotion. We develop a prototype of a synthetic 3D face that shows emotion associated to text-based speech in an automated way. As a first step we studied how many and what kind of emotional expressions produced by humans during conversations. The next, we studied the correlation between the displayed facial expressions and text. Based on these results, we developed a set of rules that describes dependencies between text and emotions by the employment of ontology. For this purpose, a 2D affective lexicon database has been built using WordNet database and the specific facial expressions are stored in a nonverbal dictionary. The results described in this paper enable affective-based multimodal fission.
Abstract: An experimental automated dialogue system that plays the role of a crisis hotline dispatcher is currently developed. Besides controlling the communication flow, this system is able to retrieve information about crisis situations from user's input. It offers a natural user interaction by the ability to perceive and respond to human emotions. The system has an emotion recognizer that is able to recognize the emotional loading from user's linguistic content. The recognizer uses a database that contains selected keywords on a 2D "arousal" and "valence" scale. The output of the system provides not only the information about the user's emotional state but also an indication of the urgency of his/her information regarding to crisis. The dialogue system is able to start a user friendly dialogue, taking care of the content, context and emotional loading of user's utterances.
Abstract: Our software demo package consists of an implementation for an automatic human emotion recognition system. The system is bi-modal and is based on fusing of data regarding facial expressions and emotion that has been extracted from speech signal. We have integrated Viola&Jones face detector (OpenCV), Active Appearance Model AAM (AAM-API) for extracting the face shape and Support Vector Machines (LibSVM) for the classification of emotion patterns. We have used Optical Flow algorithm for computing the features needed for the classification of facial expressions. Beside the integration of all processing components, the software system accommodates our implementation for the data fusion algorithm. Our C++ implementation has a working frame-rate of about 5fps.
Abstract: Humans are used to convey their thought through their (conscious or unconscious) choice of words. Some words possess emotive meaning together with their descriptive meaning. We develop a prototype of a synthetic 3D face that shows emotion associated to text-based speech in an automated way. As a first step, we studied how humans express emotions in face to face communication. Based on this study, we develop a 2D affective lexicon database and a set of rules that describes dependencies between linguistic contents and emotions. The result described in this paper proposes an initial step for developing knowledge for an affective-based multimodal fission.
Abstract: Following a soccer game is an example where clear emotions are displayed. This example is worked out for a humanoid robot which can express emotions with body language. The emotions expressed by the robot are not just stimuli-response, but are based on an affective state which shows dynamic behavior during the game. This can be live demonstrated with a Nao who follows the game sitting when the match is uneventful. As soon as the game becomes attractive, the Nao stands up, and shows his pleasure or displeasure. By showing these type of emotional expressions the robot can be seen as a sort of partner, which facilitates the cooperation with humans.
Abstract: In actor-agent teams human and artificial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can benefit from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the
realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of
pursuing ambient cognition within actor-agent systems and present the plans and expected results of the Dutch Companion project.
Abstract: In actor-agent teams human and artiﬁcial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can beneﬁt from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural social and emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of pursuing ambient cognition within actor-agent systems and brieﬂy describe the goals of the Dutch Companion project.
Abstract: This article describes a method to develop a generic approach to acquire navigation
capabilities for the standard platform of the IMAV indoor competition: the Parrot
AR.Drone. Our development is partly based on simulation, which requires both a
realistic sensor and motion model. The AR.Drone simulation model is described and
validated. Furthermore, this article describes how a visual map of the indoor environment
can be made, including the effect of sensor noise. This visual map consists of a texture
map and a feature map. The texture map is used for human navigation and the feature
map is used by the AR.Drone to localize itself. To do so, a localization method is
presented. An experiment demonstrates how well the localization works for
circumstances encountered during the IMAV competition.
Abstract: Emotion has been found to influence humans’ cognitive information processing and decision-making (Schwarz, 2000). A state of sadness, for example, is accompanied by substantive information processing, with greater attention to detail, whereas people in a happier state tend to process information more heuristically. Mobile applications or services presenting information to users, especially those used primarily in emotionally laden contexts, could adapt information
presentation to users’ current emotional state to improve compliance. This paper reports the results of an 2x2 betweensubject survey experiment (N=91) with affective state (happy vs. sad) and information presentation style (heuristic vs.
substantive) as dimensions. The results confirm that participants in a sad affective state are more likely to comply with mobile agents’ advice when information is tailored to a substantive processing style. They base decisions on substantive
information and provide longer descriptions. In contrast, people in a happy affective state prefer heuristic information. These findings reinforce the importance of affect-sensitive adaptation, especially for mobile agents in potentially emotionally laden contexts.
Abstract: The system being described in the paper presents a Web interface for a fully automatic audio-video human emotion recognition. The analysis is focused on the set of six basic emotions plus the neutral type. Different classifiers are involved in the process of face detection (AdaBoost), facial expression recognition (SVM and other models) and emotion recognition from speech (GentleBoost). The Active Appearance Model - AAM is used to get the information related to the shapes of the faces to be analyzed. The facial expression recognition is frame based and no temporal patterns of emotions are managed. The emotion recognition from movies is done separately on sound and video frames. The algorithm does not handle the dependencies between audio and video during the analysis. The methodologies for data processing are explained and specific performance measures for the emotion recognition are presented.
Abstract: Emotion influences the choice of facial expression. In a dialogue the emotional state is co-determined by the events that happen during a dialogue. To enable rich, human like expressiveness of a dialogue agent, the facial displays should show a correct expression of the state of the agent in the dialogue. This paper reports about our study in building knowledge on how to appropriately express emotions in face to face communication. We have analyzed the appearance of facial expressions and corresponding dialogue-text (in balloons) of characters of selected cartoon illustrations. From the facial expressions and dialogue-text, we have extracted independently the emotional state and the communicative function. We also collected emotion words from the dialogue-text. The emotional states (label) and the emotion words are represented along two dimensions �arousal� and �valence�. Here, the relationship between facial expressions and text were explored. The final goal of this research is to develop emotional-display rules for a text-based dialogue agent.
Abstract: The recognition of the internal emotional state of one person plays an important role in several human-related fields. Among them, human-computer interaction has recently received special attention. The current research is aimed at the analysis of segmentation methods and of the performance of
the GentleBoost classifier on emotion recognition from speech. The data set used for emotion analysis is Berlin - a database of German emotional speech. A second data set is DES – Danish Emotional Speech
data set is used for comparison purposes. Our contribution for the research community consists in a novel extensive study on the efficiency of using distinct numbers of frames per speech utterance for emotion recognition. Eventually, a set of GentleBoost 'committees' with optimal classification rates is determined based on an exhaustive study on the generated classifiers and on different types of segmentation.