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.

Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier

2017 ◽  
Vol 32 (04) ◽  
pp. 1856004 ◽  
Author(s):  
M. Sultan Zia ◽  
Majid Hussain ◽  
M. Arfan Jaffar
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Incremental Learning ◽  
Facial Expression Recognition ◽  
Expression Patterns ◽  
Training Session ◽  
Recognition System ◽  
High Accuracy ◽  
Training Data ◽  
Expression Recognition

Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expression patterns which cannot be generated and used as training data in single training session. A facial expression recognition system can maintain its high accuracy and robustness globally and for a longer period if the system possesses the ability to learn incrementally. We also propose a novel classification algorithm for multinomial classification problems. It is an efficient classifier and can be a good choice for base classifier in real-time applications. We propose a facial expression recognition system that can learn incrementally. We use Local Binary Pattern (LBP) features to represent the expression space. The performance of the system is tested on static images from six different databases containing expressions from various cultures. The experiments using the incremental learning classification demonstrate promising results.

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%.

scholarly journals Facial Expression Recognition of Portuguese using American Data as a Reference

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Catarina Iria ◽  
Rui Paixão ◽  
Fernando Barbosa
Keyword(s):  
Facial Expression ◽  
Cultural Differences ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Accuracy Rate ◽  
Data Set ◽  
Lower Accuracy

It is unknown if the ability of Portuguese in the identification of NimStim data set, which was created in America to provide facial expressions that could be recognized by untrained people, is (or not) similar to the Americans. To test this hypothesis the performance of Portuguese in the recognition of Happiness, Surprise, Sadness, Fear, Disgust and Anger NimStim facial expressions was compared with the Americans, but no significant differences were found. In both populations the easiest emotion to identify was Happiness while Fear was the most difficult one. However, with exception for Surprise, Portuguese tend to show a lower accuracy rate for all the emotions studied. Results highlighted some cultural differences.

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 EXPLORING ANALYTICAL AND HOLISTIC PROCESSING IN FACIAL EXPRESSION RECOGNITION

2020 ◽  
pp. 103-140
Author(s):  
Yakov A. Bondarenko ◽  
Galina Ya. Menshikova
Keyword(s):  
Facial Expression ◽  
Multidimensional Scaling ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Spatial Configuration ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions ◽  
Relative Contribution ◽  
The Face ◽  
Subjective Space

Background. The study explores two main processes of perception of facial expression: analytical (perception based on individual facial features) and holistic (holistic and non-additive perception of all features). The relative contribution of each process to facial expression recognition is still an open question. Objective. To identify the role of holistic and analytical mechanisms in the process of facial expression recognition. Methods. A method was developed and tested for studying analytical and holistic processes in the task of evaluating subjective differences of expressions, using composite and inverted facial images. A distinctive feature of the work is the use of a multidimensional scaling method, by which a judgment of the contribution of holistic and analytical processes to the perception of facial expressions is based on the analysis of the subjective space of the similarity of expressions obtained when presenting upright and inverted faces. Results. It was shown, first, that when perceiving upright faces, a characteristic clustering of expressions is observed in the subjective space of similarities of expression, which we interpret as a predominance of holistic processes; second, by inversion of the face, there is a change in the spatial configuration of expressions that may reflect a strengthening of analytical processes; in general, the method of multidimensional scaling has proven its effectiveness in solving the problem of the relation between holistic and analytical processes in recognition of facial expressions. Conclusion. The analysis of subjective spaces of the similarity of emotional faces is productive for the study of the ratio of analytical and holistic processes in the recognition of facial expressions.

scholarly journals Facial Expression Recognition using Deep Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1845-1849
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Data Sets ◽  
Expression Recognition ◽  
Distribution Analysis ◽  
Classification Methods ◽  
Data Set ◽  
Facial Expression Analysis ◽  
Recognition Systems

Facial expression recognition (FER) is now getting extensively popular because of its ability to predict an unknown data-set, and to its extent with some accuracy. An average human being possesses or shows seven different expressions based on the situation, namely anger, sad, happy, surprise, disgust, neutral and scared. Each individual has a unique way to express the afore-mentioned expressions and hence the term “an unknown data-set”. To identify human’s present mindset through facial expressions, many data sets are prepared based on face components (such as lips, cheek, nose, eyes and eye brows etc.,) dislocations and elasticity of all the facial parts. Many facial recognition systems are functioning on muscle distribution analysis from the mother image set’s pixel parameters. This research paper is going to present about image pre processing, facial expression learning methods, classification methods and implementation of FaceEx algorithm for facial expression analysis through FER2013 CNN data sets and Viola-Jones Principle.

scholarly journals Recognition of Facial Expressions in Individuals with Elevated Levels of Depressive Symptoms: An Eye-Movement Study

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Lingdan Wu ◽  
Jie Pu ◽  
John J. B. Allen ◽  
Paul Pauli
Keyword(s):  
Depressive Symptoms ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Right Hemisphere ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions ◽  
Depression Group ◽  
Movement Study ◽  
Comparable Accuracy

Previous studies consistently reported abnormal recognition of facial expressions in depression. However, it is still not clear whether this abnormality is due to an enhanced or impaired ability to recognize facial expressions, and what underlying cognitive systems are involved. The present study aimed to examine how individuals with elevated levels of depressive symptoms differ from controls on facial expression recognition and to assess attention and information processing using eye tracking. Forty participants (18 with elevated depressive symptoms) were instructed to label facial expressions depicting one of seven emotions. Results showed that the high-depression group, in comparison with the low-depression group, recognized facial expressions faster and with comparable accuracy. Furthermore, the high-depression group demonstrated greater leftwards attention bias which has been argued to be an indicator of hyperactivation of right hemisphere during facial expression recognition.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

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