scholarly journals Intelligent Emotion Evaluation Method of Classroom Teaching Based on Expression Recognition

2019 ◽  
Vol 14 (04) ◽  
pp. 127 ◽  
Author(s):  
Yanqiu Liang
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Evaluation Method ◽  
Teaching Method ◽  
Facial Feature ◽  
Support Vector ◽  
Expression Recognition ◽  
Feature Points ◽  
Recognition Of Facial Expressions

To solve the problem of emotional loss in teaching and improve the teaching effect, an intelligent teaching method based on facial expression recognition was studied. The traditional active shape model (ASM) was improved to extract facial feature points. Facial expression was identified by using the geometric features of facial features and support vector machine (SVM). In the expression recognition process, facial geometry and SVM methods were used to generate expression classifiers. Results showed that the SVM method based on the geometric characteristics of facial feature points effectively realized the automatic recognition of facial expressions. Therefore, the automatic classification of facial expressions is realized, and the problem of emotional deficiency in intelligent teaching is effectively solved.

scholarly journals 3D Facial Expression Recognition for Defining Users’ Inner Requirements—An Emotional Design Case Study

2019 ◽  
Vol 9 (11) ◽  
pp. 2218 ◽  
Author(s):  
Maria Grazia Violante ◽  
Federica Marcolin ◽  
Enrico Vezzetti ◽  
Luca Ulrich ◽  
Gianluca Billia ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Quality Function Deployment ◽  
Three Dimensional ◽  
Support Vector ◽  
Expression Recognition ◽  
Emotional Design ◽  
3D Data

This study proposes a novel quality function deployment (QFD) design methodology based on customers’ emotions conveyed by facial expressions. The current advances in pattern recognition related to face recognition techniques have fostered the cross-fertilization and pollination between this context and other fields, such as product design and human-computer interaction. In particular, the current technologies for monitoring human emotions have supported the birth of advanced emotional design techniques, whose main focus is to convey users’ emotional feedback into the design of novel products. As quality functional deployment aims at transforming the voice of customers into engineering features of a product, it appears to be an appropriate and promising nest in which to embed users’ emotional feedback with new emotional design methodologies, such as facial expression recognition. This way, the present methodology consists in interviewing the user and acquiring his/her face with a depth camera (allowing three-dimensional (3D) data), clustering the face information into different emotions with a support vector machine classificator, and assigning customers’ needs weights relying on the detected facial expressions. The proposed method has been applied to a case study in the context of agriculture and validated by a consortium. The approach appears sound and capable of collecting the unconscious feedback of the interviewee.

Research on the Facial Expression Feature Extraction of Facial Expression Recognition Based on MATLAB

2014 ◽  
Vol 1049-1050 ◽  
pp. 1522-1525
Author(s):  
Wang Ju ◽  
Ding Rui ◽  
Chun Yan Nie
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition ◽  
Information Communication ◽  
Facial Feature Extraction

In such a developed day of information communication, communication is an important essential way of interpersonal communication. As a carrier of information, expression is rich in human behavior information. Facial expression recognition is a combination of many fields, but also a new topic in the field of pattern recognition. This paper mainly studied the facial feature extraction based on MATLAB, by MATLAB software, extracting the expression features through a large number of facial expressions, which can be divided into different facial expressions more accurate classification .

Research on Facial Feature Detection Based on Correlation Analysis

2014 ◽  
Vol 1044-1045 ◽  
pp. 1489-1493
Author(s):  
Ming Zhou ◽  
Shu He ◽  
Yong Jun Cheng
Keyword(s):  
Facial Expression ◽  
Feature Detection ◽  
Evaluation Method ◽  
Facial Feature ◽  
Expression Recognition ◽  
3D Images ◽  
Modified Method ◽  
Innovative Methods ◽  
Combined Features ◽  
2D And 3D

In order to enhance the extraction efficiency of facial feature, the paper explores a novel defect evaluation method that uses combined features and modified method classifiers to characterize and classify the defects of facial expression. It provides a good approach to implement facial expression recognition both in 2D and 3D images. Innovative methods which are aimed at reducing the computational complexity and improving the accuracy of expression recognition are proposed.The experiments result showed the proposed method achieved lower error rate than other method.

scholarly journals Recognizing Facial Expression using PCA and Genetic Algorithm

2013 ◽  
pp. 117-120
Author(s):  
Mahima Agrawal ◽  
Shubangi. D. Giripunje ◽  
P. R. Bajaj
Keyword(s):  
Genetic Algorithm ◽  
Facial Expression ◽  
Facial Expressions ◽  
Recognition System ◽  
Video Sequences ◽  
Computational Time ◽  
Expression Recognition ◽  
Research Subject ◽  
Still Images ◽  
Recognition Of Facial Expressions

This paper presents an efficient method of recognition of facial expressions in a video. The works proposes highly efficient facial expression recognition system using PCA optimized by Genetic Algorithm .Reduced computational time and comparable efficiency in terms of its ability to recognize correctly are the benchmarks of this work. Video sequences contain more information than still images hence are in the research subject now-a-days and have much more activities during the expression actions. We use PCA, a statistical method to reduce the dimensionality and are used to extract features with the help of covariance analysis to generate Eigen –components of the images. The Eigen-components as a feature input is optimized by Genetic algorithm to reduce the computation cost.

scholarly journals Blurred Facial Expression Recognition System by Using Convolution Neural Network

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 804-816
Author(s):  
Elaf J. Al Taee ◽  
Qasim Mohammed Jasim
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Cognitive Activity ◽  
Recognition System ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions

A facial expression is a visual impression of a person's situations, emotions, cognitive activity, personality, intention and psychopathology, it has an active and vital role in the exchange of information and communication between people. In machines and robots which dedicated to communication with humans, the facial expressions recognition play an important and vital role in communication and reading of what is the person implies, especially in the field of health. For that the research in this field leads to development in communication with the robot. This topic has been discussed extensively, and with the progress of deep learning and use Convolution Neural Network CNN in image processing which widely proved efficiency, led to use CNN in the recognition of facial expressions. Automatic system for Facial Expression Recognition FER require to perform detection and location of faces in a cluttered scene, feature extraction, and classification. In this research, the CNN used for perform the process of FER. The target is to label each image of facial into one of the seven facial emotion categories considered in the JAFFE database. JAFFE facial expression database with seven facial expression labels as sad, happy, fear, surprise, anger, disgust, and natural are used in this research. We trained CNN with different depths using gray-scale images from the JAFFE database.The accuracy of proposed system was 100%.

Robust Real-Time Facial Expressions Tracking and Recognition

2014 ◽  
Vol 4 (1) ◽  
pp. 95-105 ◽  
Author(s):  
J. Zraqou ◽  
W. Alkhadour ◽  
A. Al-Nu'aimi
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Facial Feature ◽  
Face Mask ◽  
Expression Recognition ◽  
Prediction Ability ◽  
Tracking Process ◽  
Automatic Methods ◽  
Face Localization

Enabling computer systems to track and recognize facial expressions and then infer emotions from about real time video is a challenging research topic. In this work, a real time approach to emotion recognition through facial expression in live video is introduced. Several automatic methods for face localization, facial feature tracker, and facial expression recognition are employed. A robust tracking is achieved by using a face mask to resolve mismatches that could be generated during the tracking process. Action units (AUs) are then built to recognize the facial expression in each frame. The main objective of this work is to provide a prediction ability of a human behavior such as a crime, angry or for being nervous.

scholarly journals A Texture Features based Robust Facial Expression Recognition

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Pattern Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Expression Recognition ◽  
Local Descriptor ◽  
Extraction Pattern ◽  
Multiclass Support Vector Machine

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.

scholarly journals Efficient recognition of facial expressions does not require motor simulation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Gilles Vannuscorps ◽  
Michael Andres ◽  
Alfonso Caramazza
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Visual Analysis ◽  
Expression Recognition ◽  
Motor Simulation ◽  
Recognition Of Facial Expressions ◽  
Motor Representations ◽  
Efficient Recognition

What mechanisms underlie facial expression recognition? A popular hypothesis holds that efficient facial expression recognition cannot be achieved by visual analysis alone but additionally requires a mechanism of motor simulation — an unconscious, covert imitation of the observed facial postures and movements. Here, we first discuss why this hypothesis does not necessarily follow from extant empirical evidence. Next, we report experimental evidence against the central premise of this view: we demonstrate that individuals can achieve normotypical efficient facial expression recognition despite a congenital absence of relevant facial motor representations and, therefore, unaided by motor simulation. This underscores the need to reconsider the role of motor simulation in facial expression recognition.

scholarly journals Schizotypy is associated with difficulties detecting emotional facial expressions

2021 ◽  
Vol 8 (11) ◽  
Author(s):  
Shota Uono ◽  
Wataru Sato ◽  
Reiko Sawada ◽  
Sayaka Kawakami ◽  
Sayaka Yoshimura ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Task ◽  
Clinical Population ◽  
Expression Recognition ◽  
Emotional Facial Expressions ◽  
Visual Change ◽  
Recognition Of Facial Expressions ◽  
Age And Sex

People with schizophrenia or subclinical schizotypal traits exhibit impaired recognition of facial expressions. However, it remains unclear whether the detection of emotional facial expressions is impaired in people with schizophrenia or high levels of schizotypy. The present study examined whether the detection of emotional facial expressions would be associated with schizotypy in a non-clinical population after controlling for the effects of IQ, age, and sex. Participants were asked to respond to whether all faces were the same as quickly and as accurately as possible following the presentation of angry or happy faces or their anti-expressions among crowds of neutral faces. Anti-expressions contain a degree of visual change that is equivalent to that of normal emotional facial expressions relative to neutral facial expressions and are recognized as neutral expressions. Normal expressions of anger and happiness were detected more rapidly and accurately than their anti-expressions. Additionally, the degree of overall schizotypy was negatively correlated with the effectiveness of detecting normal expressions versus anti-expressions. An emotion–recognition task revealed that the degree of positive schizotypy was negatively correlated with the accuracy of facial expression recognition. These results suggest that people with high levels of schizotypy experienced difficulties detecting and recognizing emotional facial expressions.

scholarly journals Improved Facial Expression Recognition Method Based on GAN

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhuan Wang
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
High Accuracy ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
Expression Recognition ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Of Facial Expressions

Recognizing facial expressions accurately and effectively is of great significance to medical and other fields. Aiming at problem of low accuracy of face recognition in traditional methods, an improved facial expression recognition method is proposed. The proposed method conducts continuous confrontation training between the discriminator structure and the generator structure of the generative adversarial networks (GANs) to ensure enhanced extraction of image features of detected data set. Then, the high-accuracy recognition of facial expressions is realized. To reduce the amount of calculation, GAN generator is improved based on idea of residual network. The image is first reduced in dimension and then processed to ensure the high accuracy of the recognition method and improve real-time performance. Experimental part of the thesis uses JAFEE dataset, CK + dataset, and FER2013 dataset for simulation verification. The proposed recognition method shows obvious advantages in data sets of different sizes. The average recognition accuracy rates are 96.6%, 95.6%, and 72.8%, respectively. It proves that the method proposed has a generalization ability.

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