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Abstract. Facial expression and hand gesture analysis plays a fundamental part in emotionally rich man-machine interaction (MMI) systems, since it employs universally accepted non-verbal cues to estimate the users' emotional state. In this paper, we present a systematic approach to extracting expression related features from image sequences and inferring an emotional state via an intelligent rule-based system. MMI systems can benefit from these concepts by adapting their functionality and presentation with respect to user reactions or by employing agent-based interfaces to deal with specific emotional states, such as frustration or anger.

In the framework of MPEG-4, one can include applications where virtual agents, utilizing both textual and multisensory data, including facial expressions and nonverbal speech help systems become accustomed to the actual feelings of the user. Applications of this technology are expected in educational environments, virtual collaborative workplaces, communities, and interactive entertainment. Facial animation has gained much interest within the MPEG-4 framework; with implementation details being an open research area (Tekalp, 1999). In this paper, we describe a method for enriching human computer interaction, focusing on analysis and synthesis of primary and intermediate facial expressions (Ekman and Friesen (1978)). To achieve this goal, we utilize facial animation parameters (FAPs) to model primary expressions and describe a rule-based technique for handling intermediate ones. A relation between FAPs and the activation parameter proposed in classical psychological studies is established, leading to parameterized facial expression analysis and synthesis notions, compatible with the MPEG-4 standard

Abstract. In this paper we present a multimodal approach for the recognition of eight emotions that integrates information from facial expressions, body movement and gestures and speech. We trained and tested a model with a Bayesian classifier, using a multimodal corpus with eight emotions and ten subjects. First individual classifiers were trained for each modality. Then data were fused at the feature level and the decision level. Fusing multimodal data increased very much the recognition rates in comparison with the unimodal systems: the multimodal approach gave an improvement of more than 10% with respect to the most successftil unimodal system. Further, the ftision performed at the feature level showed better results than the one performed at the decision level.

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Abstract. Affective and human-centered computing have attracted a lot of attention during the past years, mainly due to the abundance of devices and environments able to exploit multimodal input from the part of the users and adapt their functionality to their preferences or individual habits. In the quest to receive feedback from the users in an unobtrusive manner, the combination of facial and hand gestures with prosody information allows us to infer the users' emotional state, relying on the best performing modality in cases where one modality suffers from noise or bad sensing conditions. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences. Contrary to strictly controlled recording conditions of audiovisual material, the proposed approach focuses on sequences taken from nearly real world situations. Recognition is performed via a 'Simple Recurrent Network' which lends itself well to modeling dynamic events in both user's facial expressions and speech. Moreover this approach differs from existing work in that it models user expressivity using a dimensional representation of activation and valence, instead of detecting discrete 'universal emotions', which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels.

This work is about multimodal and expressive synthesis on virtual agents, based on the analysis of actions performed by human users. As input we consider the image sequence of the recorded human behavior. Computer vision and image processing techniques are incorporated in order to detect cues needed for expressivity features extraction. The multimodality of the approach lies in the fact that both facial and gestural aspects of the user's behavior are analyzed and processed. The mimicry consists of perception, interpretation, planning and animation of the expressions shown by the human, resulting not in an exact duplicate rather than an expressive model of the user's original behavior. © 2007 Springer Science+Business Media B.V