Spiros Ioannou

TL;DR: A novel neurofuzzy system is created, based on rules that have been defined through analysis of FAP variations both at the discrete emotional space, as well as in the 2D continuous activation-evaluation one, that allows for further learning and adaptation to specific users' facial expression characteristics.

TL;DR: This paper presents a systematic approach to extracting expression related features from image sequences and inferring an emotional state via an intelligent rule-based system.

TL;DR: A robust and adaptable facial feature extraction system used for facial expression recognition in human-computer interaction (HCI) environments, based on a multicue feature extraction and fusion technique, which provides MPEG-4-compatible features assorted with a confidence measure.

TL;DR: This chapter presents a holistic approach to emotion modeling and analysis and their applications in Man-Machine Interaction applications, and shows that it is possible to transform quantitative feature information from video sequences to an estimation of a user’s emotional state.

TL;DR: 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.