Affective Computing and Intelligent Interaction: Second by Panrong Yin, Liyue Zhao, Lixing Huang, Jianhua Tao (auth.),

By Panrong Yin, Liyue Zhao, Lixing Huang, Jianhua Tao (auth.), Ana C. R. Paiva, Rui Prada, Rosalind W. Picard (eds.)

This publication constitutes the refereed lawsuits of the second one overseas 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 geared up in topical sections on affective facial features and popularity, affective physique expression and popularity, affective speech processing, affective textual content and discussion processing, recognising have an effect on utilizing physiological measures, computational versions of emotion and theoretical foundations, affective databases, annotations, instruments and languages, affective sound and tune processing, affective interactions: structures and functions, in addition to comparing affective systems.

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Additional info for Affective Computing and Intelligent Interaction: Second International Conference, ACII 2007 Lisbon, Portugal, September 12-14, 2007 Proceedings

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In the situations in which the data gathered in the annotation study was insufficient to make a choice, we used also the conclusions from other experiments [6,13,14]. In Table 2 we can see which pattern of facial expression i will be used for each type of emotion (positive, neutral, negative) and strategy of politeness. Table 2. 3 R. Niewiadomski and C. Pelachaud Processing The values of social power (SP) and distance (SD) and the label of an emotional state Ei are the inputs of our model. SP and SD take values from the interval [0,1].

Animations. For the purpose of the experiment we generated five different animations. Three of them correspond to different politeness strategies (positive politeness, negative politeness, and off-record in turn, see Table 2). The animations used in the experiment were constructed as follow: – Animation A1 - corresponds to low social distance and low or neutral power (negative and positive expressions are expressed freely, the fake joy is used instead of the neutral expression); – Animation A2 - corresponds to high social distance and neutral power (positive expressions are inhibited, while negative ones are masked); – Animation A3 - corresponds to high social distance and high dominance of the observer over the displayer (negative expressions are masked, positive expressions are displayed, fake joy is used instead of the neutral expression); – Animation A4 - negative expressions are masked by happiness, fake expression of sadness is used; – Animation A5 - negative expressions are expressed freely, fake expression of anger is used, happiness is masked by anger.

Such a study was conducted by Michel and el Kaliouby[14] who used the positions of feature points obtained from video 38 H. Towner and M. Slater tracking as input to a support vector machine, and then use this model to test unseen data. They report classification accuracy of about 90%. Studies that have investigated partial occlusions have typically looked at how robust an expression recognition system could be given the presence of occlusions. Two general techniques have been used for the recognition of occluded facial expressions.

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