Dynamic Face and Upper-Body Emotion Recognition for Service Robots

Vithanawasam, T.M.W.; Madhusanka, B.G.D.A.

doi:10.1109/icis.2018.8466505

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…T.M.W. Vithanawasam et al (2018) recognized face and upper-body emotions with the RGB-D (Kinect) sensor for service robots because it offers robust upper-body detection. 1000 photos were used in the dataset's creation, with each image representing a different emotion.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The sensor records 30 frames per second at a frame rate of 30 samples per second. According to the findings of facial emotion detection, the percentages of anger, fear, boredom, and neutrality were 83.3%, 79.5%, 82.1%, and 84.2%, respectively [41].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…According to [ 43 ], 65% of the humans’ non-verbal communication is achieved by body language. Commonly, technologies using body language to recognize mental states by utilizing visual recognition algorithms to differentiate between body postures, gender and age [ 44 ]. The recognition based on the body language can be considered inaccurate due to different cultural habits between people.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Eyam

Mohammed

Lastra

2021

Sensors

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The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

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Attention-Aware Recognition of Activities of Daily Living Based on Eye Gaze Tracking

Madhusanka

Ramadass

Rajagopal

et al. 2022

Internet of Things for Human-Centered Design

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Dynamic Face and Upper-Body Emotion Recognition for Service Robots

Cited by 8 publications

References 5 publications

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Attention-Aware Recognition of Activities of Daily Living Based on Eye Gaze Tracking

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