By Panrong Yin, Liyue Zhao, Lixing Huang, Jianhua Tao (auth.), Ana C. R. Paiva, Rui Prada, Rosalind W. Picard (eds.)
This ebook constitutes the refereed lawsuits of the second one foreign convention on Affective Computing and clever interplay, ACII 2007, held in Lisbon, Portugal, in September 2007.
The fifty seven revised complete papers and four revised brief papers offered including the prolonged abstracts of 33 poster papers have been rigorously reviewed and chosen from 151 submissions. The papers are equipped in topical sections on affective facial features and popularity, affective physique expression and popularity, affective speech processing, affective textual content and discussion processing, recognising impact utilizing physiological measures, computational versions of emotion and theoretical foundations, affective databases, annotations, instruments and languages, affective sound and track processing, affective interactions: platforms and purposes, in addition to comparing affective systems.
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Additional resources for Affective Computing and Intelligent Interaction: Second International Conference, ACII 2007 Lisbon, Portugal, September 12-14, 2007 Proceedings
It means that matchings ANS1 – ANS3 were in general conﬁrmed by our subjects. On the other hand, many persons provided answers diﬀerent from our expectations. In particular, the percentage of the participants that answered all questions diﬀerently to our expectations is relatively high. 7 Conclusion In this paper we described an architecture that uses facial expressions in order to express interpersonal relations of an ECA. The agent is able to mask, hide or even simulate the expression of its emotions taking into account the social context.
These types of occlusions are potentially more damaging since they result in whole features of relevance to judging facial expression being obscured. A. Paiva, R. W. ): ACII 2007, LNCS 4738, pp. 36–47, 2007. c Springer-Verlag Berlin Heidelberg 2007 Reconstruction and Recognition of Occluded Facial Expressions 2 37 Background A facial expression can be represented as a set of feature points retrieved from points across that face, for example, eye, brows, mouth. Ways in which principal component analysis (PCA) can be employed with this data is to train a PCA model with multiple sets of these points, obtained when a person is making multiple expressions or from a set of people making diﬀerent expressions.
These cases were chosen in order to emphasise the diﬀerences between diﬀerent types of interpersonal relations. The ﬁrst situation illustrates relations between two persons that are close to each other. The second situation is a typical example of a relation in which the social distance is high (Greta interacts with a stranger). Finally, in the last case our intention was to model a situation of submission. The distance is high and the opponent has a power over the displayer. e. the values of power and distance do not change.