A Framework for Automatic Human Emotion Classification Using Emotion Profiles

Mower, Emily; Matarić, Maja J.; Narayanan, Shrikanth

doi:10.1109/tasl.2010.2076804

Cited by 202 publications

(92 citation statements)

References 31 publications

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“…Each of these approaches enables processing of multi-modal data, which allows researchers to incorporate a range of characteristics (e.g., context, personality, and signals possible to record) [464]. This makes them promising for ASP applications, even outside the scope of user identification.…”

Section: User Identificationmentioning

confidence: 99%

Affective signal processing (ASP) : unraveling the mystery of emotions

Broek¹

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Section: User Identificationmentioning

confidence: 99%

Affective signal processing (ASP) : unraveling the mystery of emotions

Broek¹

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“…To fulfill this research gap, Mower et al [16,17] proposed a feature-agglomerate extraction method to encompass all appeared distinctive emotions in single prediction. Their approach coincides with the foregoing ML-ARAM model in ensuring the structured multi-label predictions.…”

Section: Previous Work On Affective Learningmentioning

confidence: 99%

Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

2018

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Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners' emotional stability in jail, can be directly benefited from our framework. Abstract:To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e., Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty-which can be quantified in Uncertainty Flow-is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our proposed Uncertainty Flow provides a glimpse of future in continuous, uncertain, and multi-label affective computing.

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“…Mower et al [27] propose an emotion classification paradigm based on emotion profiles. There have been efforts to make machines social and emotionally aware [23].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Dynamics of Relationships Between Expressed and Experienced Emotions

Srinivasan

Chander

Dam

2017

Intelligent Human Computer Interaction

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Abstract. Conversational user interfaces (CUIs) are rapidly evolving towards being ubiquitous as human-machine interfaces. Often, CUI backends are powered by a combination of human and machine intelligence, to address queries efficiently. Depending on the type of conversation issue, human-to-human conversations in CUIs (i.e. a human end-user conversing with the human in the CUI backend) could involve varying amounts of emotional content. While some of these emotions could be expressed through the conversation, others are experienced internally within the individual. Understanding the relationship between these two emotion modalities in the end-user could help to analyze and address the conversation issue better. Towards this, we propose an emotion analytic metric that can estimate experienced emotions based on its knowledge about expressed emotions in a user. Our findings point to the possibility of augmenting CUIs with an algorithmically guided emotional sense, which would help in having more effective conversations with end-users.

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A Framework for Automatic Human Emotion Classification Using Emotion Profiles

Cited by 202 publications

References 31 publications

Affective signal processing (ASP) : unraveling the mystery of emotions

Affective signal processing (ASP) : unraveling the mystery of emotions

Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

Exploring the Dynamics of Relationships Between Expressed and Experienced Emotions

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