From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Güneş, Hatice; Piccardi, Massimo; Pantić, Maja

doi:10.5772/6180

Cited by 77 publications

(46 citation statements)

References 96 publications

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“…The main topics explored in robot attributes include robot's appearance, such as Uncanny Valley (Mori, 1970;Walters et al 2008;Kanda et al 2008;Ishiguro, 2006) and Social Space (Walters et al 2006;Walters, 2008), robot's behavior such as Turn-Taking (Thórisson, 2002;Calinon and Billard, 2006;Kose-Bagci et al 2008;Breazeal, 2003b) and Contingency (Dautenhahn and Werry, 2000;Yamaoka et al 2007), the synchronization of robot's capabilities (Marek et al 2007;Katsushi et al 2008;Tetsuya et al 2008;Michalowski et al 2008;Aucouturier et al 2008;Kosuge et al 2008;Breazeal, 2001;Wahlster, 2003;Alexandersson et al 2004;Minoru et al 2006;Tatsuya et al 2008), and moreover, Affective Computing (Picard, 1995;Tao et al 2005;Pantic and Rothkrantz, 2003;Gunes et al 2008;Cohn, 2010;Cohn & Sayette, 2010). …”

Section: Robot Attributesmentioning

confidence: 99%

Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Lecture Notes in Computer Science

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In the field of human-robot interaction, socially interactive robots are often equipped with the ability to detect the affective states of users, the ability to express emotions through the use of synthetic facial expressions, speech and textual content, and the ability for imitating and social learning. Past work on creating robots that can make convincing emotional expressions has concentrated on the quality of those expressions, and on assessing people's ability to recognize them. Previous recognition studies presented the facial expressions of the robots in neutral contexts, without any strong emotional valence (e.g., emotionally valenced music or video). It is therefore worth empirically exploring whether observers' judgments of the facial cues of a robot would be affected by a surrounding emotional context. This thesis takes its inspiration from the contextual effects found on the interpretation of the expressions on human faces and computer avatars, and looks at the extent to which they also apply to the interpretation of the facial expressions of a mechanical robot head. The kinds of contexts that affect the recognition of robot emotional expressions, the circumstances under which such contextual effects occur, and the relationship between emotions and the surrounding situation, are observed and analyzed in a series of 11 experiments. In these experiments, the FACS (Facial Action Coding System) (Ekman and Friesen, 2002) was applied to set up the parameters of the servos to make the robot head produce sequences of facial expressions. Four different emotional surrounding or preceding contexts were used (i.e., recorded BBC News pieces, selected affective pictures, classical music pieces and film clips). This thesis provides evidence that observers' judgments about the facial expressions of a robot can be affected by a surrounding emotional context. From a psychological perspective, the contextual effects found on the robotic facial expressions based on the FACS, indirectly support the claims that human emotions are both 4 biologically based and socially constructed. From a robotics perspective, it is argued that the results obtained from the analyses will be useful for guiding researchers to enhance the expressive skills of emotional robots in a surrounding emotional context. This thesis also analyzes the possible factors contributing to the contextual effects found in the original 11 experiments.Some future work, including four new experiments (a preliminary experiment designed to identify appropriate contextual materials and three further experiments in which factors likely to affect a context effect are controlled one by one) is also proposed in this thesis.

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Section: Robot Attributesmentioning

confidence: 99%

Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Lecture Notes in Computer Science

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“…Setting may heavily influence how people experience and engage with content, with real-world contexts producing different facial behavior than expected from lab studies [7]. This may be due to the subject being restricted in terms of motion, or limited due to social expectations, or simply affected by an unfamiliar room, device or people [8]. As an example, Fridlund [9] shows that viewer's responses to videos are influenced by context, responses in social situations are heightened when compared to responses in non-social settings.…”

Section: Fig 1: Overview Of What the User Experience Was Like Andmentioning

confidence: 99%

Crowdsourcing facial responses to online videos: Extended abstract

McDuff

Kaliouby

Picard

2015

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Traditional observational research methods required an experimenter's presence in order to record videos of participants, and limited the scalability of data collection to typically less than a few hundred people in a single location. In order to make a significant leap forward in affective expression data collection and the insights based on it, our work has created and validated a novel framework for collecting and analyzing facial responses over the Internet. The first experiment using this framework enabled 3,268 trackable face videos to be collected and analyzed in under two months. Each participant viewed one or more commercials while their facial response was recorded and analyzed. Our data showed significantly different intensity and dynamics patterns of smile responses between subgroups who reported liking the commercials versus those who did not. Since this framework appeared in 2011, we have collected over three million videos of facial responses in over 75 countries using this same methodology, enabling facial analytics to become significantly more accurate and validated across five continents. Many new insights have been discovered based on crowdsourced facial data, enabling Internet-based measurement of facial responses to become reliable and proven. We are now able to provide large-scale evidence for gender, cultural and age differences in behaviors. Today such methods are used as part of standard practice in industry for copy-testing advertisements and are increasingly used for online media evaluations, distance learning, and mobile applications.

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“…Techniques used to quantify affective responses using objectively manifested responses include analysis of facial expressions (Zeng, Pantic, Roisman, and Huang 2009) and electroencephalography (Schaaff 2009). The challenge with these techniques is that the features taken as a proxy for the underlying construct need to be identified and signals from different sources meaningfully combined (Gunes, Piccardi, and Pantic, 2008). …”

Section: Introductionmentioning

confidence: 99%

Beyond usability: designing for consumers' product experience using the Rasch model

Camargo

Henson

2015

Journal of Engineering Design

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Many manufacturers wish to relate the physical properties of their products to customers' affective responses so that they can improve the design of their products. Rasch measurement theory is a novel approach in this context that could offer business benefits over other approaches. The aim of the research was to determine whether Rasch measurement theory can support the construction of a scale to measure the affective impression of a moisturizer cream elicited by the compliance of the moisturizer's packaging. The compliances of packaging used to develop a scale for the impression of the contents of the packaging in previous research were measured. New packaging prototypes were manufactured with a range of compliances to determine whether affective responses to them could be predicted. A new sample of sixty seven participants rated the prototypes against Likert statements on a five-point scale. The statements were derived from descriptions of the users' product experience. The scale was recalibrated using a multi-facet Rasch model. It was found that the affective responses to the prototypes fell within the range predicted by the original scale. The results demonstrate that Rasch measurement theory can be used to achieve the objective correspondence between affective responses and the physical properties of a product, and the information can be used in new product development.

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From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Cited by 77 publications

References 96 publications

Contextual Recognition of Robot Emotions

Contextual Recognition of Robot Emotions

Crowdsourcing facial responses to online videos: Extended abstract

Beyond usability: designing for consumers' product experience using the Rasch model

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