Abstract—Affective Computing is currently one of the most active research topics, having increasingly intensive attention. This strong interest is driven by a wide. Affective computing. The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions”. Affective computing is an emerging interdisciplinary research field bringing . Research in the affective computing field is continuing to attract.
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action. Affective computing, coupled with new wear- able computers, will also provide the ability to gather new data necessary for advances in emotion and cog -. PDF | 75 minutes read | Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong . Affective Computing, Emotion Expression, Synthesis and Recognition, Edited by Jimmy Or p. cm. pflegeelternnetz.info Oh, J.
Guilford Press. Context-Sensitive HCI: Skip to main content. Although ma- recognition. Wierzbicka, A. Furthermore, it has been shown could change not only how professionals practice that the comprehension of a given emotion label and computing, but also how mass consumers conceive the ways of expressing the related affective state and interact with the technology. Open access peer-reviewed 5.
Categorical perception of facial expressions. Camurri, A. Tao, J.: Eurospeech , Geneva September Google Scholar. Cowie, R.: Emotion recognition in human-computer interaction. Azarbayejani, A. Pavlovic, V. A Review. Gavrila, D. The Visual Analysis of Human Movement: A Survey. Aggarwal, J. Human Motion Analysis: Moriyama, T. Emotion Recognition and Synthesis System on Speech.
Antonio, R. Brain and Language. Scientific American, 89—95 September Google Scholar. Calder, A. Massaro, D. Picture My Voice: Yamamoto, E.
Lip movement synthesis from speech based on Hidden Markov Models. Gutierrez-Osuna, R. IEEE Trans. Hong, P. Real-time speech-driven face animation with expressions using neural networks. Murat Tekalp, A. Signal Processing: Bregler, C.
Video Rewrite: Driving Visual Speech with Audio. Hunt, A. Unit selection in a concatenative speech synthesis system using a large speech database. Cosatto, E. Audio-visual unit selection for the synthesis of photo-realistic talking-heads. Ezzat, T.
Verma, A. Animating Expressive Faces Across Languages. Given that we not only use the face but also body movements to express ourselves, in the second section Chapters 8 to 11 we present a research on perception and generation of emotional expressions by using full-body motions.
The third section of the book Chapters 12 to 16 presents computational models on emotion, as well as findings from neuroscience research.
In the last section of the book Chapters 17 to 22 we present applications related to affective computing. By Hamit Soyel and Hasan Demirel. By Fadi Dornaika and Franck Davoine. By Ursula Hess, Reginald B. Adams, Jr. Karthigayan, M. Rizon, R. Nagarajan and Sazali Yaacob.
By Winand H. Dittrich and Anthony P. By Celso de Melo and Ana Paiva. By Rita Ciceri and Stefania Balzarotti. By Mincheol Whang and Joasang Lim.
Afzulpurkar and Takeaki Uno. By Toni Vanhala and Veikko Surakka. By Joshua M. Susskind, Geoffrey E. This will be highly valuable in situations where firm insensitivity of current HCI designs is fine for well- attention to a crucial but perhaps tedious task is codified tasks. It works for making plane reserva- essential, such as aircraft control, air traffic control, tions, buying and selling stocks, and, as a matter of nuclear power plant surveillance, or simply driving a fact, almost everything we do with computers today.
An auto- But this kind of categorical computing is inappropri- mated tool could provide prompts for better perfor- ate for design, debate, and deliberation.
A mechanism for detecting scenes states of a person with whom we are communicating or frames that contain expressions of pain, rage, and is the core of emotional intelligence. Emotional fear could provide a valuable tool for violent-con- intelligence EQ is a facet of human intelligence tent-based indexing of movies, video material, and that has been argued to be indispensable and even digital libraries.
When it comes to computers, human affective feedback could expand and en- however, not all of them will need emotional intelli- hance research and applications include specialized gence, and none will need all of the related skills that areas in professional and scientific sectors.
Monitor- we need. Yet man-machine interactive systems ing and interpreting affective behavioral cues are capable of sensing stress, inattention, and heedful- important to lawyers, police, and security agents ness, and capable of adapting and responding appro- who are often interested in issues concerning decep- priately to these affective states of the user are likely tion and attitude. Affective Computing Table 1. The main problem areas in the research on affective computing What is an affective state?
This question is related to psychological issues pertaining to the nature of affective states and the way affective states are to be described by an automatic analyzer of human affective states. What kinds of evidence warrant conclusions about affective states? In other words, which human communicative signals convey messages about an affective arousal? This issue shapes the choice of different modalities to be integrated into an automatic analyzer of affective feedback.
How can various kinds of evidence be combined to generate conclusions about affective states? This question is related to neurological issues of human sensory- information fusion, which shape the way multi-sensory data is to be combined within an automatic analyzer of affective states. Social now used. It would also facilitate research in areas constructivists argue that emotions are socially con- such as behavioral science in studies on emotion structed ways of interpreting and responding to and cognition , anthropology in studies on cross- particular classes of situations and that they do not cultural perception and production of affective states , explain the genuine feeling affect.
Also, there is no neurology in studies on dependence between emo- consensus on how affective displays should be la- tional abilities impairments and brain lesions , and beled Wierzbicka, The main issue here is psychiatry in studies on schizophrenia in which that of culture dependency; the comprehension of a reliability, sensitivity, and precision are persisting given emotion label and the expression of the related problems.
THE PROBLEM ing the same communicative signals in the same DOMAIN way, nor is it certain that a particular modulation of interactive cues will be interpreted always in the While all agree that machine sensing and interpreta- same way independent of the situation and the tion of human affective information would be quite observer.
The immediate implication is that prag- beneficial for manifold research and application matic choices e.
In other words, all non- interactive modalities sight, sound, and touch and verbal communicative signals i. Yet the reported recognized cross-culturally.
On the other hand, there research does not confirm this assumption. The is now a growing body of psychological research visual channel carrying facial expressions and the that strongly challenges the classical theory on emo- auditory channel carrying vocal intonations are widely tion. Russell argues that emotion in general thought of as most important in the human recogni- can best be characterized in terms of a multi- tion of affective feedback.
The characteristics of an ideal automatic human-affect analyzer multimodal modalities: As a result, of efforts in affective computing concern automatic analysis of the perceived information is highly robust analysis of facial displays.
For an exhaustive survey and flexible.
Hence, in order to accomplish a of studies on machine analysis of facial affect, the multimodal analysis of human interactive signals ac- readers are referred to Pantic and Rothkrantz Yet, given that humans detect six combined only at the end of the intended analysis, as basic emotional facial expressions with an accu- the majority of current studies do.
Characteristics of currently existing automatic facial affect analyzers handle a small set of posed prototypic facial expressions of six basic emotions from portraits or nearly-frontal views of faces with no facial hair or glasses, recorded under good illumination. An interesting point, nevertheless, is that we of one to 12 words.
Namely, in spite of repeated references to the Relatively few of the existing works combine need for a readily accessible reference set of images different modalities into a single system for human image sequences that could provide a basis for affective state analysis.
Examples are the works of benchmarks for efforts in automatic facial affect Chen and Huang , De Silva and Ng , analysis, no database of images exists that is shared Yoshitomi et al. In brief, these studies assume clean only, without regard to the manner in which it was audiovisual input e. Though aspect of the speech. Yet, in contrast to spoken audio and image processing techniques in these language processing, which has witnessed signifi- systems are relevant to the discussion on the state of cant advances in the last decade, vocal expression the art in affective computing, the systems them- analysis has not been widely explored by the audi- selves have all as well as some additional draw- tory research community.
For a survey of studies on backs of single-modal affect analyzers and, in turn, automatic analysis of vocal affect, the readers are need many improvements, if they are to be used for referred to Pantic and Rothkrantz Yet humans can recognize emo- tion does not suffice.