Human-Robot Interaction based on Facial Expression Imitation

Esfandbod, Alireza; Rokhi, Zeynab; Taheri, Alireza; Alemi, Minoo; Meghdari, Ali

doi:10.1109/icrom48714.2019.9071837

Cited by 6 publications

(8 citation statements)

References 33 publications

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“…The innovative approach described in [26] employs novel video pre-processing techniques in conjunction with a convolutional neural network (CNN) to analyse human emotions based on facial expression recognition (FER). In [27], the authors suggested a system for imitating face cues using a CNN. They implemented it on a robotic platform to enable the robot to mimic its user"s emotional states when this robot looks into the user"s face.…”

Section: Face-basedmentioning

confidence: 99%

“…[82] Custom colour ball dataset Primary Colour ball datasets are composed of approximately 500 photos that have been gathered and labelled. [27], [94], [75], [9], [84], [31], [45], [46], [33], [41], [42], [43], [50], [38], [39], [53] Extended Cohn-Kanade (CK+) Secondary It is a database of facial expressions that contains 327 annotated video sequences from 123 participants in eight different expression states. The dataset"s subjects are diverse in age, gender and ethnic origin, making it one of the most preferred datasets for FER research.…”

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

“…The image has a resolution of 640 × 490 pixels. The researcher in [27] concentrated on five fundamental emotional states of CK+: happiness, sadness, surprise, anger and neutral. [28], [99], [33], [42], [44], [35], [39], [52], [53], [54], [37] Fer2013 Visual Geometry Group (VGG )datasets.…”

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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This study presents a systematic review of the automatic vision-based assessment systems and models from related fields over the last 6 years. Many studies focus on automatic vision-based assessment in the area of robotics, artificial intelligence and virtual reality or augmented reality (AR) to enhance the communication between humans and machines for general purposes. Three reliable databases, IEEEXplore, Science Direct and Web of Science, were used to obtain relevant studies on the given topic. Several stages of filtering and scanning were applied according to the inclusion/exclusion criteria to filter the obtained 3505 papers from 2015 to 2020. Finally, 97 papers met the criteria. They were classified into four main categories based on their field by following the scientific taxonomy: computer-vision-based, robotics-based, AR-based and hybrid-based categories, accounting for 42.26% (n = 41/97 papers), 48.45% (n = 47/97 papers), 6.18% (n = 6/97 papers) and 4.12% (n = 4/97 papers), respectively. Subsequently, a deep and critical analysis of this multifield systematic review highlighted new research opportunities, motivations, challenges and recommendations that need attention to integrate interdisciplinary studies. Thus, automatic vision-based assessment, which is a field requiring automated solutions, tools and methods, enhances the ability of the assistive technology and facilitates the interaction of individuals with machines. Many studies have been conducted on the automatic vision-based assessment systems and their subtypes to promote accurate communication and performance evaluation in human-machine interaction. This study can provide researchers with useful guides and valuable information for future research. This study also addresses the ambiguity of automatic vison-based assessment models in interdisciplinary fields.

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Section: Face-basedmentioning

confidence: 99%

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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“…Accordingly, numerous studies have examined facial expressions of emotions during HRI [e.g. 2,8,15,[17][18][19]33,38,50,81,81,91,91,110,116].…”

Section: Introductionmentioning

confidence: 99%

Facial Emotion Expressions in Human–Robot Interaction: A Survey

Rawal

Stock‐Homburg

2022

Int J of Soc Robotics

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Facial expressions are an ideal means of communicating one’s emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human–robot interaction has been discussed leading to several possible directions for future research.

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“…Accordingly, numerous studies have examined facial expressions of emotions during HRI [e.g. 18,88,111,8,79,15,2,105,17,19,32,37,49,79,88]. In any HHI, human beings first infer the emotional state of the other person and then accordingly generate facial expressions in response to their peer.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion expressions in human-robot interaction: A survey

Rawal¹,

Stock‐Homburg²

2021

Preprint

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Facial expressions are an ideal means of communicating one's emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real time will be covered. For robotic facial expression generation, hand coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real time is comparatively lower. In case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically.

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Human-Robot Interaction based on Facial Expression Imitation

Cited by 6 publications

References 33 publications

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Facial Emotion Expressions in Human–Robot Interaction: A Survey

Facial emotion expressions in human-robot interaction: A survey

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