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 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 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 types of emotion and theoretical foundations, affective databases, annotations, instruments and languages, affective sound and track processing, affective interactions: structures and purposes, 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
As the experimental result reveals, it is the second order polynomial function that receive the best fitting result, and the mathematic form is shown in Equation 2. e. [P2, A2, D2], α and β are the corresponding coefficient matrix, δ is the constant offset vector. It should be noticed that, each dimension of PEP vector is estimated respectively with the same mathematic form as equation 3 shown. The PEPi is the i-th dimension in PEP vector, and all the corresponding function coefficients are indicated with the subscript i and P-A-D dimensions.
Typically, images or videos of the top or bottom half of the face are presented for judgement and the recognition rates are compared with each other and with the full face recognition for the same faces. Studies[10,11,12] showed that the facial expressions of half faces were less accurately classiﬁed than full face ones with facial expressions from the lower half of the face being more accurately identiﬁed than facial expressions from the upper half of the face; however, some speciﬁc expressions were more accurately predicted from the upper half of the face than the lower – such expressions termed recognizable-top by Calder – examples being sadness and fear.
Based on our previous work on FAP-driven facial expression synthesis [1,9], we propose the Partial Expression Parameters (PEPs) to depict the common expression movement within specific face regions, such as mouth-bent, eye-open and 26 S. Zhang et al. eyebrow-raise etc. The PEPs capture the correlation among different FAPs, and thus reduces the complexity of FAP-driven expression synthesis . With PAD as high-level emotion description, PEP as mid-level expression configuration and FAP as low-level animation parameter, a layered framework for PAD-driven facial expression synthesis is proposed as shown in Figure 1, where the PAD-PEP mapping model is trained on a pseudo facial expression database with PAD and PEP annotations, and the PEP-FAP translation model is defined experimentally using a homegrown expression editor .
Affective Computing and Intelligent Interaction: Second International Conference, ACII 2007 Lisbon, Portugal, September 12-14, 2007 Proceedings by Panrong Yin, Liyue Zhao, Lixing Huang, Jianhua Tao (auth.), Ana C. R. Paiva, Rui Prada, Rosalind W. Picard (eds.)