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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.

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This chapter presents a holistic approach to emotion modeling and analysis and their applications in Man-Machine Interaction applications. Beginning from a symbolic representation of human emotions found in this context, based on their expression via facial expressions and hand gestures, we show that it is possible to transform quantitative feature information from video sequences to an estimation of a user’s emotional state. While these features can be used for simple representation purposes, in our approach they are utilized to provide feedback on the users’ emotional state, hoping to provide next-generation interfaces that are able to recognize the emotional states of their users.

An intelligent emotion recognition system, interweaving psychological findings about emotion representation with analysis and evaluation of facial expressions has been generated and its performance has been investigated with experimental real data. Additionally, a fuzzy rule based system has been created for classifying facial expressions to the six archetypal emotion categories. The continuous 2-D emotion space was then examined and a pool of known and novel classification and clustering techniques have been applied to our data obtaining high rates in classification and clustering into quadrants of the emotion representation space.

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Current multimedia databases contain a wealth of information in the form of audiovisual, as well as text data. Even though efficient search algorithms have been developed for either media, there still exists the need for abstract presentation and summarization of the results of database users' queries. Moreover, multimedia retrieval systems should be capable of providing the user with additional information related to the specific subject of the query, as well as suggest other topics which users with a similar profile are interested in. In this paper, we present a number of solutions to these issues, giving as an example an integrated architecture we have developed, along with notions that support efficient and secure Internet access and easy addition of new material. Segmentation of the video in shots is followed by shot classification in a number of predetermined categories. Generation of users' profiles according to the same categories, enhanced by relevance feedback, permits an efficient presentation of the retrieved video shots or characteristic frames in terms of the user interest in them. Moreover, this clustering scheme assists the notion of "lateral" links that enable the user to continue retrieval with data of similar nature or content to those already returned. Furthermore, user groups are formed and modeled by registering actual preferences and practices; this enables the system to "predict" information that is possibly relevant to specific users and present it along with the returned results. The concepts utilized in this system can be smoothly integrated in MPEG-7 compatible multimedia database systems.

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