scholarly journals Feature extraction and classification methods of facial expression: a survey

2021 ◽  
Vol 2 (1) ◽  
pp. 26-32
Author(s):  
Moe Moe Htay
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Human Computer Interaction ◽  
Affective Computing ◽  
Recognition System ◽  
Expression Recognition ◽  
Care Assessment ◽  
Main Components ◽  
Fatigue Detection ◽  
Computer Interaction

Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

scholarly journals Real Time Facial Expression Recognition System Based on Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4047-4051
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Or Education ◽  
Face Expression ◽  
Active Research ◽  
Main Components

The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2013 ◽  
pp. 42-55
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Video Sequences ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

scholarly journals “Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset

2021 ◽  
Author(s):  
Michael J Lyons
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Human Computer Interaction ◽  
Present Article ◽  
Face Perception ◽  
Affective Computing ◽  
The Creation ◽  
Computer Interaction ◽  
Training Sets

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay “Excavating AI: The Politics of Images in Machine Learning Training Sets.” The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen’s fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.

scholarly journals Region based Facial Expression Recognition using Gradient Directions

2020 ◽  
Vol 9 (4) ◽  
pp. 2771-2776
Keyword(s):  
Facial Expression ◽  
Template Matching ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Recognition System ◽  
Research Field ◽  
Expression Recognition ◽  
Emotion Classification ◽  
Classification Result ◽  
Basic Emotions

Facial Expression Recognition (FER) has gained significant importance in the research field of Affective Computing in different extents. As a part of the different dimensional thinking, aiming at improving the accuracy of the recognition system and reducing the computational load, region based FER is proposed in this paper. The system is an emotion identifying system among the basic emotions, through subject independent template matching based on gradient directions. The model designed is tested on the Enhanced Cohn-Kanade (CK+) dataset. Another important contribution of the work is using only eye (including eyebrows and the nose portion near eyes) and mouth regions in the emotion recognition. The emotion classification result is 94.3% (CK+ dataset) for 6-class FER.

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