Affective computing

About: Affective computing is a research topic. Over the lifetime, 3453 publications have been published within this topic receiving 89020 citations.

Affective Computing

Abstract: Computers are beginning to acquire the ability to express and recognize affect, and may soon be given the ability to " have emotions. " The essential role of emotion in both human cognition and perception, as demonstrated by recent neurological studies, indicates that affective computers should not only provide better performance in assisting humans, but also might enhance computers' abilities to make decisions. This paper presents and discusses key issues in " affective computing, " computing that relates to, arises from, or influences emotions. Models are suggested for computer recognition of human emotion, and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction. Affective computing, coupled with new wear-able computers, will also provide the ability to gather new data necessary for advances in emotion and cog-nition theory. Nothing in life is to be feared. It is only to be understood. – Marie Curie Emotions have a stigma in science; they are believed to be inherently non-scientific. Scientific principles are derived from rational thought, logical arguments, testable hypotheses, and repeatable experiments. There is room alongside science for " non-interfering " emotions such as those involved in curiosity, frustration, and the pleasure of discovery. In fact, much scientific research has been prompted by fear. Nonetheless, the role of emotions is marginalized at best. Why bring " emotion " or " affect " into any of the deliberate tools of science? Moreover, shouldn't it be completely avoided when considering properties to design into computers? After all, computers control significant parts of our lives – the phone system, the stock market, nuclear power plants, jet landings, and more. Who wants a computer to be able to " feel angry " at them? To feel contempt for any living thing? In this essay I will submit for discussion a set of ideas on what I call " affective computing, " computing that relates to, arises from, or influences emotions. This will need some further clarification which I shall attempt below. I should say up front that I am not proposing the pursuit of computerized cingulotomies 1 or even into the business of building " emotional computers ". 1 The making of small wounds in the ridge of the limbic system known as the cingulate gyrus, a surgical procedure to aid severely depressed patients. Nor will I propose answers to the difficult and intriguing questions , " …

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Abstract: We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Abstract: Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

Toward machine emotional intelligence: analysis of affective physiological state

Abstract: The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.

Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

Abstract: This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their effectiveness. Hence, an informed and integrated examination of emotion theories from multiple areas will need to become part of computing practice if truly effective real-world systems are to be achieved. This survey discusses theoretical perspectives that view emotions as expressions, embodiments, outcomes of cognitive appraisal, social constructs, products of neural circuitry, and psychological interpretations of basic feelings. It provides meta-analyses on existing reviews of affect detection systems that focus on traditional affect detection modalities like physiology, face, and voice, and also reviews emerging research on more novel channels such as text, body language, and complex multimodal systems. This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. In this way, models and methods can be compared, and emerging insights from various disciplines can be more expertly integrated.