AutisMitr: Emotion Recognition Assistive Tool for Autistic Children

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.

Download Full-text

Real-time facial feature extraction and emotion recognition

Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint ◽

10.1109/icics.2003.1292676 ◽

2004 ◽

Cited By ~ 9

Author(s):

L.C. De Silva ◽

Suen Chun Hui

Keyword(s):

Feature Extraction ◽

Emotion Recognition ◽

Real Time ◽

Facial Feature ◽

Facial Feature Extraction

Download Full-text

Weighting Facial Features Extraction using Geometric Average

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v5i1.206 ◽

2001 ◽

Vol 6 (1) ◽

pp. 39-44

Author(s):

Saparudin Saparudin

Keyword(s):

Facial Feature ◽

Recognition System ◽

Facial Features ◽

Important Process ◽

Facial Feature Extraction ◽

Geometric Average ◽

Face Recognition System ◽

The Face ◽

Human Eyes

Human facial feature extraction is an important process in the face recognition system. The quality of the results from the extraction of human facial features is determined by the degree of accuracy. The weighting of human facial features is used to test the accuracy of the methods used. This research produces the process of weighting the facial features automatically. The results obtained are the same as those seen by the human eyes.

Download Full-text

Weighting Facial Features Extraction using Geometric Average

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v6i1.206 ◽

2001 ◽

Vol 6 (1) ◽

pp. 39-44

Author(s):

Saparudin Saparudin

Keyword(s):

Facial Feature ◽

Recognition System ◽

Facial Features ◽

Important Process ◽

Facial Feature Extraction ◽

Geometric Average ◽

Face Recognition System ◽

The Face ◽

Human Eyes

Download Full-text

Real-time facial feature extraction using statistical shape model and Haar-wavelet based feature search

2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763) ◽

10.1109/icme.2004.1394506 ◽

2005 ◽

Author(s):

Fei Zuo ◽

P.H.N. de With

Keyword(s):

Feature Extraction ◽

Real Time ◽

Facial Feature ◽

Haar Wavelet ◽

Statistical Shape Model ◽

Shape Model ◽

Feature Search ◽

Facial Feature Extraction ◽

Statistical Shape

Download Full-text

Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.2.480 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

Hady Pranoto ◽

Oktaria Kusumawardani

Keyword(s):

Face Recognition ◽

Real Time ◽

Success Factors ◽

Recognition System ◽

Expression Recognition ◽

Student Attendance ◽

Face Recognition System ◽

Triplet Loss ◽

Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

Download Full-text

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

Applied Sciences ◽

10.3390/app112210540 ◽

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.

Download Full-text