Face to interface

Schiano, Diane J.; Ehrlich, Sheryl M.; Rahardja, K.; Sheridan, Kyle

doi:10.1145/332040.332430

Cited by 33 publications

(2 citation statements)

References 13 publications

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“…Bartneck (2001) also investigated the effect of level of abstraction (natural human face versus computer rendered face) on both the convincingness and distinctiveness of facial expressions of affect and found that expressions conveyed by both types of face were equally convincing but it was the synthetic face that produced expressions significantly more distinct than the complex human face. Similar results have been found in studies examining the perception of facial expressions displayed by virtual agents or avatars (Pandzic & Forcheimer, 2002) and robots (Schiano et al, 2000) as long as the synthetic characters' emotional expression is adequately expressed in critical areas of the face (Tinwell et al, 2011). Cartoon-like, synthetic faces have been found to yield very high ER rates (approximately 90%) for the 6 basic facial expressions (Kobayashi & Hara, 1996).…”

Section: Synthetic Facessupporting

confidence: 81%

The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces

Lawrence

Nabi

2013

International Journal of Synthetic Emotions

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The BARTA (Bolton Affect Recognition Tri-Stimulus Approach) is a unique database comprising over 400 colour images of the universally recognised basic emotional expressions and is the first compilation to include three different classes of validated face stimuli; emoticon, computer-generated cartoon and photographs of human faces. The validated tri-stimulus collection (all images received =70% inter-rater (child and adult) consensus) has been developed to promote pioneering research into the differential effects of synthetic emotion representation on atypical emotion perception, processing and recognition in autism spectrum disorders (ASD) and, given the recent evidence for an ASD synthetic-face processing advantage (Rosset et al., 2008), provides a means of investigating the benefits associated with the recruitment of synthetic face images in ASD emotion recognition training contexts.

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Section: Synthetic Facessupporting

confidence: 81%

The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces

Lawrence

Nabi

2013

International Journal of Synthetic Emotions

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“…In human-human interaction, face-to-face and verbal communication are the inherently natural ways in which affective information is communicated. 86,87 Similarly, in the service sector, a robot needs to be able to establish the most natural possible interaction with people, understanding their affective state. Physiological signals are the least implemented modality in this context.…”

Section: Application Of Affective Computing In Hri: Other Contextsmentioning

confidence: 99%

Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing

Gervasi

Barravecchia

Mastrogiacomo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, that is, capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g. facial expressions, body language, voice, or physiological signals) and to adapt its behavior accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g. healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector.

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Selective Fidelity: Investigating Priorities for the Creation of Expressive Avatars

Garau

Computer Supported Cooperative Work

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Face to interface

Cited by 33 publications

References 13 publications

The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces

The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces

Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing

Selective Fidelity: Investigating Priorities for the Creation of Expressive Avatars

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