Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen, Luefeng; Liu, Zhentao; Wu, Min; Deng, Min; Dong, Fangyan; Hirota, Kaoru

doi:10.1007/s12369-015-0290-2

Cited by 30 publications

(10 citation statements)

References 38 publications

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“…It can be used to recognise the most likely sequence of cognitive states of a speaker, given his or her multimodal activity, and to predict the most likely sequence of the following activities. Finally, Chen et al [12] conducted experiments in a "drinking at a bar" scenario. In contrast to our user engagement classification, their intention recognition system with two-layer fuzzy support vector regression identifies most likely orders based on age, gender, nationality, and detected emotions.…”

Section: Related Workmentioning

confidence: 99%

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

Foster

Gaschler

Giuliani

2017

Int J of Soc Robotics

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A robot agent designed to engage in real-world human-robot joint action must be able to understand the social states of the human users it interacts with in order to behave appropriately. In particular, in a dynamic public space, a crucial task for the robot is to determine the needs and intentions of all of the people in the scene, so that it only interacts with people who intend to interact with it. We address the task of estimating the engagement state of customers for a robot bartender based on the data from audiovisual sensors. We begin with an offline experiment using hidden Markov models, confirming that the sensor data contains the information necessary to estimate user state. We then present two strategies for online state estimation: a rule-based classifier based on observed human behaviour in real bars, and a set of supervised classifiers trained on a labelled corpus. These strategies are compared in offline cross-validation, in an online user study, and through validation against a separate test corpus. These studies show that while the trained classifiers are best in a cross-validation setting, the rule-based classifier performs best with novel data; however, all classiThis article integrates and extends the work described in the following conference papers: [17,20,23,24 Bristol Robotics Laboratory, University of the West of England, Bristol, UK fiers also change their estimate too frequently for practical use. To address this issue, we present a final classifier based on Conditional Random Fields: this model has comparable performance on the test data, with increased stability. In summary, though, the rule-based classifier shows competitive performance with the trained classifiers, suggesting that for this task, such a simple model could actually be a preferred option, providing useful online performance while avoiding the implementation and data-scarcity issues involved in using machine learning for this task.

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Section: Related Workmentioning

confidence: 99%

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

Foster

Gaschler

Giuliani

2017

Int J of Soc Robotics

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“…It is well‐known that these optimization algorithms take a larger time to converge. Chen et al 10 used two layer fuzzy support vector regression in human–robot interaction. The input data are classified using the Fuzzy c‐means clustering and intention understanding is done using emotion, through using the information such as age, gender, and nationality.…”

Section: Related Workmentioning

confidence: 99%

A novel geometric fuzzy membership functions for mouth and eye brows to recognize emotions

Priya

Bharat

2019

Concurrency and Computation

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Summary In this article, the novel geometrical fuzzy membership functions are proposed to accurately recognize the emotions. The four corner features are taken from both the eyebrows and mouth regions of the expressive static image without considering reference face. The quadrilateral shape is defined from the features and it failed to match with the basic geometric shapes. This quadrilateral is given to the proposed novel fuzzy membership functions to calculate the degree of impreciseness and vagueness exists in the defined quadrilateral. As the result, the 12 fuzzy features are obtained and evaluated by the various classifiers. Both CK and JAFFE datasets are used in the experiment to prove the performance of the proposed method compared to some of the recent methods. It is observed the proposed method gives the highest accuracy rate of 98.4% and 99.15% against contemporary methods. The proposed work achieves the statistical measures of 97.8% of sensitivity, 99.2% of specificity, 97.9% of positive predictive value, and 97.8% of F‐measure. Moreover, the proposed work achieves encouraging results compared to the recent Google API using publicly available datasets. This achievement is due to the fact of exactly identified the impreciseness and vagueness in the defined quadrilateral. The proposed approach avoids reference face and uses the least number of fuzzy features compared to state‐of‐the‐art approaches that reduces the pre‐processing, computational, and learning times.

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“…An intention understanding model based on two layers fuzzy support vector regression is adopted to comprehend humans' inner thoughts in our previous work [14], which includes two parts, i.e. local learning layer and global learning layer.…”

Section: Demand Analysis Based On Deep Cognitive Informationmentioning

confidence: 99%

“…To realise initiative service in HRI, communication information of human is necessary, which is divided into two parts, i.e. surface communication information and deep cognitive information [14, 15]. Surface communication information refers to the information that can be obtained directly by sensors or the combination of knowledge base, e.g.…”

Section: Introductionmentioning

confidence: 99%

Proposal of initiative service model for service robot

Hao

Cao

et al. 2017

CAAI Transactions on Intelligence Technology

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Service robots are becoming a part of today's life. To realise friendly human-robot interaction, an initiative service model for service robot is proposed, which is composed of three layers, i.e. information acquisition layer, in which communication information and environmental information are collected by sensors; decision layer, in which personal intention and emotional states are obtained by multi-modal emotion recognition and intention understanding, and users' demands are obtained by demand analysis model; initiative service execution layer, in which the task execution and emotion adaption of robot are carried out. Initiative service can sense motive of people, by which a humanised and real-time service could be realised. Drinking service experiments are performed, including passive service and initiative service of robots. Experimental results show that the drinking time of passive service lags behind the initiative service 30 min on average, and the initiative service renders drinking service 2 times more than passive service on average, besides, the water quantity provided by initiative service is more reasonable, which demonstrates the feasibility of the proposal. In prospect, initiative service model could be applied to many occasions in people's daily life, e.g. family service, caring for the elderly, and medical rehabilitation.

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Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Cited by 30 publications

References 38 publications

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

A novel geometric fuzzy membership functions for mouth and eye brows to recognize emotions

Proposal of initiative service model for service robot

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