Facial Expression Recognition and Positive Emotion Incentive System for Human-Robot Interaction

Hu, Chen; Gu, Yu; Wang, Fei; Sheng, Weihua

doi:10.1109/wcica.2018.8630711

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Robots are trained to process images and give appropriate feedback once analyzed all the captured images. Authors [3] have proposed PEIS model trains the robots to process real-life images. Features are extracted from the captured images using the FERW model to give correct feedback.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated CNN-BiLSTM Approach for Facial Expressions

Pansambal,

Nandgaokar,

Rajput

et al. 2024

IJACSA

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Deep learning algorithms have demonstrated good performance in many sectors and applications. Facial expression recognition (FER) is recognizing the emotions through images. FER is an integral part of many applications. With the help of the CNN-BiLSTM integrated approach, higher accuracy can be achieved in identification of the facial expressions. Convolutional neural networks (CNN) consist of a Conv2D layer, dividing the given images into batches, performing normalization and if required flattening the data i.e. converting the data in a 1D array and achieving a higher accuracy. BiLSTM works on two LSTMs i.e. one in the forward direction and the other in a backward direction. One can use LSTM to process the images (datasets) however, it is suggested with the help of BiLSTM can predict the expressions with more accuracy. Input data is available in both the direction (forward and backward) which helps maintaining the context. Using LSTM CNN and BiLSTM always helps increasing the prediction accuracy. Application areas where a BiLSTM can give more prediction accuracy are the forecasting models, text recognition, speech recognition, classifying the large data and the proposed facial expression recognition. The integrated approach (CNN and BiLSTM) increases the accuracy significantly as discussed in the results and discussion section.This approach could be categorized as a fusion technique where two methods (approaches) are integrated to get higher accuracy. The results and discussion section elaborates the effectiveness of the integrated approach compared to HERO: human emotions recognition for realizing the intelligent internet of things. As compared to the HERO approach CNN-BiLSTM gives good results in terms of precision and recall.

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Section: Literature Reviewmentioning

confidence: 99%

An Integrated CNN-BiLSTM Approach for Facial Expressions

Pansambal,

Nandgaokar,

Rajput

et al. 2024

IJACSA

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show abstract

“…The robots that are used in the studies are either robotic heads or humanoid robots such as Pepper, Nao, iCub etc. Many studies that perform facial expression recognition in real-time use CNNs, making it a popular choice for facial expression recognition [2,2,8,15,133]. However, the highest accuracy is achieved by Bayesian and Artificial Neural Network (ANN) methods for facial expression recognition in real-time.…”

Section: Fer In Real-timementioning

confidence: 99%

“…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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“…The robots that are used in the studies are either robotic heads or humanoid robots such as Pepper, Nao, iCub etc. Many studies that perform facial expression recognition in real-time use CNNs, making it a popular choice for facial expression recognition [8,128,15,2,2]. However, the highest accuracy in achieved by Bayesian and Artificial Neural Network (ANN) methods for facial expression recognition in realtime.…”

Section: Fer In Real-timementioning

confidence: 99%

“…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.

show abstract

Facial Expression Recognition and Positive Emotion Incentive System for Human-Robot Interaction

Cited by 10 publications

References 7 publications

An Integrated CNN-BiLSTM Approach for Facial Expressions

An Integrated CNN-BiLSTM Approach for Facial Expressions

Facial Emotion Expressions in Human–Robot Interaction: A Survey

Facial emotion expressions in human-robot interaction: A survey

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