scholarly journals Real Time Multimodal Emotion Recognition System using Facial Landmarks and Hand over Face Gestures

2017 ◽  
Vol 7 (2) ◽  
pp. 30-34 ◽  
Author(s):  
Mahesh Krishnananda Prabhu Prabhu ◽  
◽  
Dinesh Babu Jayagopi ◽  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Facial Landmarks ◽  
Multimodal Emotion Recognition

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.

Smart IoT Multimodal Emotion Recognition System Using Deep Learning Networks

2021 ◽  
pp. 3-19
Author(s):  
V. J. Aiswaryadevi ◽  
G. Priyanka ◽  
S. Sathya Bama ◽  
S. Kiruthika ◽  
S. Soundarya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Recognition System ◽  
Learning Networks ◽  
Multimodal Emotion Recognition

Real-Time Electroencephalography-Based Emotion Recognition System

2016 ◽  
Vol 11 (5) ◽  
pp. 456 ◽  
Author(s):  
Riyanarto Sarno ◽  
Muhammad Nadzeri Munawar ◽  
Brilian T. Nugraha
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Recognition System

scholarly journals AutisMitr: Emotion Recognition Assistive Tool for Autistic Children

2020 ◽  
Vol 10 (1) ◽  
pp. 259-269
Author(s):  
Akansha Singh ◽  
Surbhi Dewan
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Facial Feature ◽  
Recognition System ◽  
Face Identification ◽  
Positive Outcomes ◽  
Autistic Children ◽  
Facial Feature Extraction ◽  
Three Stages

AbstractAssistive technology has proven to be one of the most significant inventions to aid people with Autism to improve the quality of their lives. In this study, a real-time emotion recognition system for autistic children has been developed. Emotion recognition is implemented by executing three stages: Face identification, Facial Feature extraction, and feature classification. The objective is to frame a system that includes all three stages of emotion recognition activity that executes expeditiously in real time. Thus, Affectiva SDK is implemented in the application. The propound system detects at most 7 facial emotions: anger, disgust, fear, joy, sadness, contempt, and surprise. The purpose for performing this study is to teach emotions to individuals suffering from autism, as they lack the ability to respond appropriately to others emotions. The proposed application was tested with a group of typical children aged 6–14 years, and positive outcomes were achieved.

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