Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most s...

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Autores principales:	Navjot Rathour, Zeba Khanam, Anita Gehlot, Rajesh Singh, Mamoon Rashid, Ahmed Saeed AlGhamdi, Sultan S. Alshamrani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	emotion recognition face detection face recognition machine learning (ML) real-time systems Raspberry-Pi Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c2d067e329924ecab970e2700a83d136
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spelling	oai:doaj.org-article:c2d067e329924ecab970e2700a83d1362021-11-25T16:31:03ZReal-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi10.3390/app1122105402076-3417https://doaj.org/article/c2d067e329924ecab970e2700a83d1362021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10540https://doaj.org/toc/2076-3417There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.Navjot RathourZeba KhanamAnita GehlotRajesh SinghMamoon RashidAhmed Saeed AlGhamdiSultan S. AlshamraniMDPI AGarticleemotion recognitionface detectionface recognitionmachine learning (ML)real-time systemsRaspberry-PiTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10540, p 10540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	emotion recognition face detection face recognition machine learning (ML) real-time systems Raspberry-Pi Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	emotion recognition face detection face recognition machine learning (ML) real-time systems Raspberry-Pi Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Navjot Rathour Zeba Khanam Anita Gehlot Rajesh Singh Mamoon Rashid Ahmed Saeed AlGhamdi Sultan S. Alshamrani Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
description	There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.
format	article
author	Navjot Rathour Zeba Khanam Anita Gehlot Rajesh Singh Mamoon Rashid Ahmed Saeed AlGhamdi Sultan S. Alshamrani
author_facet	Navjot Rathour Zeba Khanam Anita Gehlot Rajesh Singh Mamoon Rashid Ahmed Saeed AlGhamdi Sultan S. Alshamrani
author_sort	Navjot Rathour
title	Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
title_short	Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
title_full	Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
title_fullStr	Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
title_full_unstemmed	Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
title_sort	real-time facial emotion recognition framework for employees of organizations using raspberry-pi
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c2d067e329924ecab970e2700a83d136
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Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

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