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.

Present work introduces a probabilistic recognition scheme for hand gestures. Self organizing feature maps are used to model spatiotemporal information extracted through image processing. Two models are built for each gesture category and, along with appropriate distance metrics, produce a validated classification mechanism that performs consistently during experiments on acted gestures video sequences.

Abstract-Optical tracking systems have become particularly popular in virtual studios applications tending to substitute electromechanical ones. However, optical systems are reported to be inferior in terms of accuracy in camera motion estimation. Moreover, marker-based approaches often cause problems in image/video compositing and impose undesirable constraints on camera movement. Present work introduces a novel methodology for the construction of a two-tone blue screen, which allows the localization of camera in three-dimensional (3-D) space on the basis of the captured sequence. At the same time, a novel algorithm is presented for the extraction of camera's 3-D motion parameters based on 3-D-to-two-dimensional (2-D) line correspondences. Simulated experiments have been included to illustrate the performance of the proposed system.