Evaluation and Discussion of Multi-modal Emotion Recognition

Rabie, Ahmad; Wrede, Britta; Vogt, Thurid; Hanheide, Marc

doi:10.1109/iccee.2009.192

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…The person independent one uses a general emotional profile for all possible interaction partners. While The person-specific method outperforms the person independent method, the latter exhibits more validity than the former [2], [3].…”

Section: Introductionmentioning

confidence: 94%

Multi-modal biometrics for real-life person-specific emotional human-robot-interaction

Rabie

Handmann

2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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Currently in home environments, robot assisting systems with emotion understanding ability are generally achieved in two several manners. The first is the implementing of such systems in such a way that they offer general services for all considered persons without considering privacy, special needs of their interaction partners. The second way is the targetting of such systems for merely one person. In this work we present a robot assisting system, which has both the abilities of assisting several persons at the same time and sustaining their privacy and security issues. The robot can interact with it's interaction partner emotionally by analyzing the emotions of her expressed either visually, facial expression, or auditive, speech prosody. The role of this system is the providing of person-specific support in home environment. In order to identify its interaction partner the system uses diverse biometric traits. According to the recognized ID the system, first, adopts towards the needs of recognized person. Second the system loads the corresponding emotional profile of the detected interaction partner in order to practice a person-specific emotional humanrobot interaction, which has an advantage over the person independent interaction.

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Section: Introductionmentioning

confidence: 94%

Multi-modal biometrics for real-life person-specific emotional human-robot-interaction

Rabie

Handmann

2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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“…However, many existing methods use exaggerated expression of prototypical emotions and have not used a natural environment [7,8]. On the other hand, facial expressions do not necessarily reflect specific emotions, as facial expressions are usually ambiguous and very dependent on the person's character.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Frustration Detection on Smartphones

Vasiete

Yeh

2015

Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

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“…We use active appearance models (AAMs) [12] for the extraction of facial features, which were also utilized by many others [13], [14], [15]. For comparison, we also performed some experiments with constrained local models (CLMs) [16], [17], which are also widely used for face recognition.…”

Section: Related Workmentioning

confidence: 99%

Facial communicative signal interpretation in human-robot interaction by discriminative video subsequence selection

Lang

Wachsmuth

Hanheide

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-Facial communicative signals (FCSs) such as head gestures, eye gaze, and facial expressions can provide useful feedback in conversations between people and also in humanrobot interaction. This paper presents a pattern recognition approach for the interpretation of FCSs in terms of valence, based on the selection of discriminative subsequences in video data. These subsequences capture important temporal dynamics and are used as prototypical reference subsequences in a classification procedure based on dynamic time warping and feature extraction with active appearance models. Using this valence classification, the robot can discriminate positive from negative interaction situations and react accordingly. The approach is evaluated on a database containing videos of people interacting with a robot by teaching the names of several objects to it. The verbal answer of the robot is expected to elicit the display of spontaneous FCSs by the human tutor, which were classified in this work. The achieved classification accuracies are comparable to the average human recognition performance and outperformed our previous results on this task.

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Evaluation and Discussion of Multi-modal Emotion Recognition

Cited by 15 publications

References 15 publications

Multi-modal biometrics for real-life person-specific emotional human-robot-interaction

Multi-modal biometrics for real-life person-specific emotional human-robot-interaction

Multimodal Frustration Detection on Smartphones

Facial communicative signal interpretation in human-robot interaction by discriminative video subsequence selection

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