Emotional affordances in human-machine interactive planning and negotiation

Franzoni, Valentina; Milani, Alfredo; Vallverdú, Jordi

doi:10.1145/3106426.3109421

Cited by 26 publications

(18 citation statements)

References 15 publications

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“…The main problem in facial emotion recognition is the lack of proper datasets of images for training. Most of the available datasets take into consideration only the Ekman model [ 9 ] or its subsets [ 5 ], discarding more complex and complete models, such as Plutchik [ 10 ], or models based on emotional affordance [ 11 ]. Moreover, image datasets often contain non-genuine expressions (e.g., simulated by professional actors) rather than spontaneous and natural forms of facial expression [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

Franzoni

Biondi

Perri

et al. 2020

Sensors

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This work concludes the first study on mouth-based emotion recognition while adopting a transfer learning approach. Transfer learning results are paramount for mouth-based emotion emotion recognition, because few datasets are available, and most of them include emotional expressions simulated by actors, instead of adopting real-world categorisation. Using transfer learning, we can use fewer training data than training a whole network from scratch, and thus more efficiently fine-tune the network with emotional data and improve the convolutional neural network’s performance accuracy in the desired domain. The proposed approach aims at improving emotion recognition dynamically, taking into account not only new scenarios but also modified situations to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in a healthcare management environment, and portable applications supporting disabled users having difficulties in seeing or recognising facial emotions. This achievement takes advantage of previous preliminary works on mouth-based emotion recognition using deep-learning, and has the further benefit of having been tested and compared to a set of other networks using an extensive dataset for face-based emotion recognition, well known in the literature. The accuracy of mouth-based emotion recognition was also compared to the corresponding full-face emotion recognition; we found that the loss in accuracy is mostly compensated by consistent performance in the visual emotion recognition domain. We can, therefore, state that our method proves the importance of mouth detection in the complex process of emotion recognition.

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Section: Introductionmentioning

confidence: 99%

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

Franzoni

Biondi

Perri

et al. 2020

Sensors

Self Cite

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show abstract

“…The main problem in facial-based emotion recognition is the lack of proper data sets of images for training. Most of the available data sets take in consideration only the Ekman model [6] or its subsets [5], making actually nearly impossible to use more complex and complete models, such as Plutchik [7], or models based on emotional affordance [8]. Moreover, the main problem of the various image data set is that they contain non-genuine expressions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Mouth-based Emotion Recognition using Transfer Learning

Franzoni

Biondi

Perri

et al. 2020

Preprint

Self Cite

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The paper concludes the first research on mouth-based Emotion Recognition (ER), adopting a Transfer Learning (TL) approach. Transfer Learning results paramount for mouth-based emotion ER, because a few data sets are available, and most of them include emotional expressions simulated by actors, instead of adopting a real-world categorization. Using TL we can use fewer training data than training a whole network from scratch, thus more efficiently fine-tuning the network with emotional data and improving the convolutional neural network accuracy in the desired domain. The proposed approach aims at improving the Emotion Recognition dynamically, taking into account not only new scenarios but also modified situations with respect to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in an healthcare management environment, or portable applications supporting disabled users having difficulties in seeing or recognizing facial emotions. This work takes advantage from previous preliminary works on mouth-based emotion recognition using CNN deep-learning, and has the further benefit of testing and comparing a set of networks on large data sets for face-based emotion recognition well known in literature. The final result is not directly comparable with works on full-face ER, but valorizes the significance of mouth in emotion recognition, obtaining consistent performances on the visual emotion recognition domain.

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“…The research on the characteristics of the spatial interaction network in social networks and their evolutionary mechanisms is of great significance for providing location-based business services, planning and managing communication network facilities, and formulating regional economic development policies. In addition, the results also can be used to improve the performances of several types of applications in various fields, such as social network analysis [ 10 ] and affective computing [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Evolution Model of Spatial Interaction Network in Online Social Networking Services

Dong

Chen

Zhang

et al. 2019

Entropy

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The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities.

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Emotional affordances in human-machine interactive planning and negotiation

Cited by 26 publications

References 15 publications

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

Enhancing Mouth-based Emotion Recognition using Transfer Learning

Evolution Model of Spatial Interaction Network in Online Social Networking Services

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