Emotion Recognition using Facial Expressions in Children using the NAO Robot

Lopez-Rincon, Alejandro

doi:10.1109/conielecomp.2019.8673111

Cited by 32 publications

(22 citation statements)

References 33 publications

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“…For their foundation, these studies rely on contributions from robotics (e.g., [1,11,156]) and HRI [121,122,169,225]. Further studies are rooted in human-computer interaction (e.g., [3,4,21,99,140,173], engineering [171], and philosophy [101].…”

Section: Framework Of the Overviewmentioning

confidence: 99%

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Stock‐Homburg

2021

Int J of Soc Robotics

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Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

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Section: Framework Of the Overviewmentioning

confidence: 99%

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Stock‐Homburg

2021

Int J of Soc Robotics

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“…This toolkit is one of the most used toolkits that is a cross-platform to recognize multi-face expression in real-time by using the facial action coding system (FACS). It has been showing a high accuracy and reliable system for several application [38][39][40]. The length of time for each recognized emotions was calculated and analyzed to determine if the emotions presented in the robot's voice reflected on the emotional states of users.…”

Section: Experiments Bmentioning

confidence: 99%

Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study

Lee

2021

Applied Sciences

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The use of affective speech in robotic applications has increased in recent years, especially regarding the developments or studies of emotional prosody for a specific group of people. The current work proposes a prosody-based communication system that considers the limited parameters found in speech recognition for the elderly, for example. This work explored what types of voices were more effective for understanding presented information, and if the affects of robot voices reflected on the emotional states of listeners. By using functions of a small humanoid robot, two different experiments conducted to find out comprehension level and the affective reflection respectively. University students participated in both tests. The results showed that affective voices helped the users understand the information, as well as that they felt corresponding negative emotions in conversations with negative voices.

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“…There are very few studies have developed models for classifying CAFE [5][6][7][8] . Rather than focusing on prototypic expressions, many of these studies classify all images in the database 5,6 .…”

Section: Background Reviewmentioning

confidence: 99%

“…With the growing application of human-computer interaction (HCI), it has also become important to develop custom user and age specific systems for facial expression recognition. Although there is a great body of literature focused on applying machine learning and deep learning techniques to the classification of facial expressions produced by adults 1 , few works apply these methods to the facial expressions produced by children [5][6][7][8] . Automated methods for the classification of facial expressions produced by children are an important component for the development of HCI systems that target child users, especially those designed for treatment, intervention, or training of children.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning approach to multiclass classification of child facial expressions

Witherow

Samad

Iftekharuddin

2019

Applications of Machine Learning

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The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a dearth of literature in child facial expression analysis. Recent advances in transfer learning methods have enabled the use of deep learning architectures, trained on adult facial expression images, to be tuned for classifying child facial expressions with limited training samples. The network will learn generic facial expression patterns from adult expressions which can be fine-tuned to capture representative features of child facial expressions. This work proposes a transfer learning approach for multi-class classification of the seven prototypical expressions including the 'neutral' expression in children using a recently published child facial expression data set. This work holds promise to facilitate the development of technologies that focus on children and monitoring of children throughout their developmental stages to detect early symptoms related to developmental disorders, such as Autism Spectrum Disorder (ASD).

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Emotion Recognition using Facial Expressions in Children using the NAO Robot

Cited by 32 publications

References 33 publications

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study

Transfer learning approach to multiclass classification of child facial expressions

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