By Ana Paiva, Rui Prada, Rosalind W. Picard
This booklet constitutes the refereed court cases 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 awarded including the prolonged abstracts of 33 poster papers have been conscientiously 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 have an effect on utilizing physiological measures, computational versions of emotion and theoretical foundations, affective databases, annotations, instruments and languages, affective sound and song processing, affective interactions: structures and functions, in addition to comparing affective systems.
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Extra info for Affective Computing and Intelligent Interaction: Second International Conference, ACII 2007, Lisbon, Portugal, September 12-14, 2007, Proceedings
Detailed partial expression description for PEP can be found in Table 1. The value of the PEP is a scalar ranging in [-1, 1] to simulate the continuous facial expression change. 2) is illustrated in Figure 2 with its value ranging from -1 to +1. 2 PEP-FAP Translation Template Previous research on FAPs correlation  indicates that there are high correlations among different FAPs, and this cross-correlation property has been successfully exploited to interpolate the unknown FAP value from a set of decided FAPs.
Sample number of 7 expressions (labels in the first row) in each PEP clustering center (Ci in the first column) C1 C2 C3 C4 C5 C6 C7 Total C1 HAP 25 4 2 0 0 0 0 31 C2 C3 C4 C5 C6 C7 SUP 1 20 0 6 0 3 0 30 ANG 2 1 18 1 0 0 8 30 NEU 0 0 0 30 0 0 0 30 SAD 1 0 9 0 15 4 2 31 FEAR 0 3 4 0 9 11 5 32 DIS Total 0 29 1 29 45 12 0 37 2 26 2 20 27 12 29 213 Fig. 5. 2 for horizontal jaw movement) in the database. The FAPU shown in Figure 4(c) is utilized to normalize the extracted PEP to eliminate the individual difference caused by face shape of different subjects.
The training set, which consists of the rest 193 expression image samples, is then divided into 10 subsets with each subset covering all 10 subjects and each subject with 1-2 expressions. By such division scheme, we are able to capture the common facial expression movement shared by different people as much as possible in the PADPEP mapping function. The coefficients of the PAD-PEP mapping function is estimated using the least square errors method. g. PEPi) is estimated separately with the same form as shown in equation 3, which means that we finally have 12 sets of coefficients corresponding to 12 PEP parameters.