IEEE Transaction on Affective Computing (TAC) is an academic journal published by IEEE. (ISSN 1949-3045).
Affective computing is the field of study concerned with understanding, recognizing and utilizing human emotions in the design of computational systems. Research in the area is motivated by the fact that emotion pervades human life – emotions motivate human behavior, they promote social bonds between people and between people and artifacts, and emotional cues play an important role in forecasting human mental state and future actions. Technology is less efficient if it perturbs human emotions; more efficient if it engages with them productively; more attractive if it appeals to human emotions; and often it is primarily concerned with enabling humans to experience particular emotions (notably happiness). Since the coining of the term by Picard in 1997, affective computing has emerged as a cohesive sub-discipline in computer science with its own international conference (the International Conference on Affective Computing and Intelligent Interaction) and professional society (the HUMAINE Association).
IEEE Transactions on Affective Computing is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. The journal will publish original research on the principles and theories explaining why and how affective factors condition interaction between humans and technology, on how affective sensing and simulation techniques can inform our understanding of human affective processes, and on the design, implementation and evaluation of systems that carefully consider affect among the factors that influence their usability. Surveys of existing work will be considered for publication when they propose a new viewpoint on the history and the perspective on this domain. The journal covers but is not limited to the following topics:
Sensing and analysis
Algorithms and features for the recognition of affective state from face and body gestures
Analysis of text and spoken language for emotion recognition
Analysis of prosody and voice quality of affective speech
Recognition of auditory and visual affect bursts
Recognition of affective state from central (e.g. fMRI, EEG) and peripheral (e.g. GSR) physiological measures
Methods for multi-modal recognition of affective state
Recognition of group emotion
Methods of data collection with respect to psychological issues as mood induction and elicitation or technical methodology as motion capturing
Tools and methods of annotation for provision of emotional corpora
(Cyber)Psychology and behavior
Clarification of concepts related to ‘affective computing' (e.g., emotion, mood, personality, attitude) in ways that facilitate their use in computing.
Computational models of human emotion processes (e.g., decision-making models that account for the influence of emotion; predictive models of user emotional state)
Studies on cross-cultural, group and cross-language differences in emotional expression
Contributions to standards and markup language for affective computing
Behavior generation and user interaction
Computational models of visual, acoustic and textual emotional expression for synthetic and robotic agents
Models of verbal and nonverbal expression of various forms of affect that facilitate machine implementation
Methods to adapt interaction with technology to the affective state of users
Computational methods for influencing the emotional state of people
New methods for defining and evaluating the usability of affective systems and the role of affect in usability
Methods of emotional profiling and adaptation in mid- to long-term interaction
Application of affective computing including education, health care, entertainment, customer service, design, vehicle operation, social agents/robotics, affective ambient intelligence, customer experience measurement, multimedia retrieval, surveillance systems, biometrics, music retrieval and generation
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