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

Applied Sciences

Zeba Khanam¹  Ahmed Saeed AlGhamdi²  Mamoon Rashid³  Sultan S. Alshamrani⁴  Navjot Rathour⁵  Rajesh Singh⁵  Anita Gehlot⁵ 

[1] College of Computing and Informatics, Saudi Electronic University, Dammam 15515, Saudi Arabia;Department of Computer Engineering, College of Computer and Information Technology, Taif University, PO Box 11099, Taif 21994, Saudi Arabia;Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India;Department of Information Technology, College of Computer and Information Technology, Taif University, PO Box 11099, Taif 21944, Saudi Arabia;School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144001, India;

关键词: emotion recognition;    face detection;    face recognition;    machine learning (ML);    real-time systems;    Raspberry-Pi;   

DOI  :  10.3390/app112210540

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown