An Automatic Emotion Recognition System for Annotating Spotify’s Songs

2019 ◽  
pp. 345-362 ◽  
Author(s):  
J. García de Quirós ◽  
S. Baldassarri ◽  
J. R. Beltrán ◽  
A. Guiu ◽  
P. Álvarez
Keyword(s):  
Emotion Recognition ◽  
Recognition System

scholarly journals Implementation of A New Speech Negative Emotion Recognition System

2021 ◽  
Vol 1757 (1) ◽  
pp. 012021
Author(s):  
Yuqiong Wang ◽  
Zehui Zhao ◽  
Zhiwei Huang
Keyword(s):  
Emotion Recognition ◽  
Negative Emotion ◽  
Recognition System

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

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

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.

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