By Ana Paiva, Rui Prada, Rosalind W. Picard
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 provided 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 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
Another emotion labeling experiment is also conducted where the subjects are required to select one word that best describe the synthetic expression from 14 candidate emotion words, which are Happy, Optimism, Relax, Surprise, Mildness, Dependent, Bored, Sad, Scared, Anxious, Scornful, Disgusting, Angry, and Hostile. 22] Fig. 8. PAD-driven synthetic expression for selected emotions (Emotion [P,A,D]) Table 8. Result of PAD evaluation and emotion labeling. 22]). 44 Expression Label (with voting percent) Happy (67%) Surprise (100%) Sad (42%) Sad (50%) Hostile (58%) Disgusting (50%) The PAD values for each basic emotion word and the PAD perception values for synthetic expression are summarized in Table 8.
Power (SP) and social distance (SD) are two factors that inﬂuence human expressions according to various studies about facial behaviour [6,13,20]. Facial behaviour management is also conditioned by emotional factors. In particular, facial behaviour depends on the valence (Val) of emotion [6,14]. Negative emotions are more often masked or inhibited, while positive emotions are often pretended. Thus, in our model, we consider three variables to encompass the characteristics of interaction and features of emotional state of the displayer, namely: social distance (SD), social power (SP), and valence of emotion (Val).
69 In the K-fold cross-validation training process (k=10), there are 10 iterations corresponding to 10 validating subset. For each iteration, we calculate the correlation coefficients between real and estimate data on current validating subset as criteria to evaluate the fitting performance of the trained function. The minimum, maximum and average value of correlation coefficient is summarized in Table 7. The trained function with the average fitting performance among all the 10 iterations is chosen as the final result and used to evaluate the test set.