About this Research Topic
Affective computing refers to computing that relates to, arises from, or influences emotions, as pioneered by Rosalind Picard in 1995. The goal of affective computing is to bridge the gap between human and machines and ultimately enable robots to communicate with human naturally and emotionally. Recently, the research on affective computing has gained considerable progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing mainly focuses on estimating of human emotions through different forms of signals, e.g., face video, EEG, Speech, PET scans or fMRI.
Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.
We encourage researchers from the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research fields, such as facial expression recognition, body language recognition, human physiological signal (heart rate) estimation, human-robot interaction, multimodal affective computing et al, aiming for improved affective computing algorisms. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems.
This Research Topic aims to bring together research including, but not limited to:
1) learning with few labeled exemplars and unlabeled images or videos for affective computing tasks such as facial (micro) expression recognition, facial action unit detection, remote heart rate estimation, gaze estimation, and many others.
2) novel learning methods to alleviate discrete emotion annotation ambiguities.
3) neuro-inspired methods that are capable of enhancing cross-dataset and cross-domain generalizability of DNN models on affective computing.
4) enabling human-robot interaction by supervised/unsupervised or reinforcement learning methods.
5) applications of affective computing in robotics, such as autonomous robots.
Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.
We encourage researchers from the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research fields, such as facial expression recognition, body language recognition, human physiological signal (heart rate) estimation, human-robot interaction, multimodal affective computing et al, aiming for improved affective computing algorisms. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems.
This Research Topic aims to bring together research including, but not limited to:
1) learning with few labeled exemplars and unlabeled images or videos for affective computing tasks such as facial (micro) expression recognition, facial action unit detection, remote heart rate estimation, gaze estimation, and many others.
2) novel learning methods to alleviate discrete emotion annotation ambiguities.
3) neuro-inspired methods that are capable of enhancing cross-dataset and cross-domain generalizability of DNN models on affective computing.
4) enabling human-robot interaction by supervised/unsupervised or reinforcement learning methods.
5) applications of affective computing in robotics, such as autonomous robots.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
