scholarly journals Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4847
Author(s):  
Vianney Perez-Gomez ◽  
Homero V. Rios-Figueroa ◽  
Ericka Janet Rechy-Ramirez ◽  
Efrén Mezura-Montes ◽  
Antonio Marin-Hernandez
Keyword(s):  
Facial Expressions ◽  
Principal Component ◽  
Selection Method ◽  
Data Sets ◽  
Geometric Features ◽  
Expression Recognition ◽  
Coding System ◽  
Recognition Of Facial Expressions ◽  
Comparable Accuracy ◽  
2D And 3D

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.

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.

Facial Expression Recognition

2011 ◽  
pp. 255-317 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Muscle Group ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Multiple Cultures ◽  
Expression Of Emotions ◽  
Objective Description

The facial expression has long been an interest for psychology, since Darwin published The expression of Emotions in Man and Animals (Darwin, C., 1899). Psychologists have studied to reveal the role and mechanism of the facial expression. One of the great discoveries of Darwin is that there exist prototypical facial expressions across multiple cultures on the earth, which provided the theoretical backgrounds for the vision researchers who tried to classify categories of the prototypical facial expressions from images. The representative 6 facial expressions are afraid, happy, sad, surprised, angry, and disgust (Mase, 1991; Yacoob and Davis, 1994). On the other hand, real facial expressions that we frequently meet in daily life consist of lots of distinct signals, which are subtly different. Further research on facial expressions required an object method to describe and measure the distinct activity of facial muscles. The facial action coding system (FACS), proposed by Hager and Ekman (1978), defines 46 distinct action units (AUs), each of which explains the activity of each distinct muscle or muscle group. The development of the objective description method also affected the vision researchers, who tried to detect the emergence of each AU (Tian et. al., 2001).

The current challenges of automatic recognition of facial expressions: A systematic review

2020 ◽  
Vol 33 (3-6) ◽  
pp. 113-138
Author(s):  
Audrey Masson ◽  
Guillaume Cazenave ◽  
Julien Trombini ◽  
Martine Batt
Keyword(s):  
Systematic Review ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Future Research ◽  
Expression Recognition ◽  
Review Of The Literature ◽  
Significant Progress ◽  
Recognition Of Facial Expressions ◽  
The Subject ◽  
Current Systems

In recent years, due to its great economic and social potential, the recognition of facial expressions linked to emotions has become one of the most flourishing applications in the field of artificial intelligence, and has been the subject of many developments. However, despite significant progress, this field is still subject to many theoretical debates and technical challenges. It therefore seems important to make a general inventory of the different lines of research and to present a synthesis of recent results in this field. To this end, we have carried out a systematic review of the literature according to the guidelines of the PRISMA method. A search of 13 documentary databases identified a total of 220 references over the period 2014–2019. After a global presentation of the current systems and their performance, we grouped and analyzed the selected articles in the light of the main problems encountered in the field of automated facial expression recognition. The conclusion of this review highlights the strengths, limitations and main directions for future research in this field.

A Study on Improved Real-Time Eye Region Detection Method for More Realistic Facial Expressions Recognition

2011 ◽  
Vol 268-270 ◽  
pp. 471-475
Author(s):  
Sungmo Jung ◽  
Seoksoo Kim
Keyword(s):  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Detection Method ◽  
Expression Recognition ◽  
Improved Method ◽  
Region Detection ◽  
Recognition Of Facial Expressions ◽  
The Face ◽  
Facial Expressions Recognition ◽  
Eye Region

Many 3D films use technologies of facial expression recognition. In order to use the existing technologies, a large number of markers shall be attached to a face, a camera is fixed in front of the face, and movements of the markers are calculated. However, the markers calculate only the changes in regions where the markers are attached, which makes difficult realistic recognition of facial expressions. Therefore, this study extracted a preliminary eye region in 320*240 by defining specific location values of the eye. And the final eye region was selected from the preliminary region. This study suggests an improved method of detecting an eye region, reducing errors arising from noise.

scholarly journals Facial Expression Recognition Using Fast Walidlet Hybrid Transform

2020 ◽  
pp. 59-69
Author(s):  
Walid Mahmod ◽  
Jane Stephan ◽  
Anmar Razzak
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Good Accuracy ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Artificial Intelligence Research ◽  
Multiwavelet Transform

Automatic analysis of facial expressions is rapidly becoming an area of intense interest in computer vision and artificial intelligence research communities. In this paper an approach is presented for facial expression recognition of the six basic prototype expressions (i.e., joy, surprise, anger, sadness, fear, and disgust) based on Facial Action Coding System (FACS). The approach is attempting to utilize a combination of different transforms (Walid let hybrid transform); they consist of Fast Fourier Transform; Radon transform and Multiwavelet transform for the feature extraction. Korhonen Self Organizing Feature Map (SOFM) then used for patterns clustering based on the features obtained from the hybrid transform above. The result shows that the method has very good accuracy in facial expression recognition. However, the proposed method has many promising features that make it interesting. The approach provides a new method of feature extraction in which overcome the problem of the illumination, faces that varies from one individual to another quite considerably due to different age, ethnicity, gender and cosmetic also it does not require a precise normalization and lighting equalization. An average clustering accuracy of 94.8% is achieved for six basic expressions, where different databases had been used for the test of the method.

scholarly journals Brain-Inspired Deep Networks for Facial Expression Recognition

2020 ◽  
Author(s):  
Nafiseh Zeinali ◽  
Karim Faez ◽  
Sahar Seifzadeh
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Data Augmentation ◽  
Data Sets ◽  
Expression Recognition ◽  
Face Expression ◽  
Public Data ◽  
Improve Accuracy ◽  
Left And Right ◽  
Increase In Accuracy

Purpose: One of the essential problems in deep-learning face recognition research is the use of self-made and less counted data sets, which forces the researcher to work on duplicate and provided data sets. In this research, we try to resolve this problem and get to high accuracy. Materials and Methods: In the current study, the goal is to identify individual facial expressions in the image or sequence of images that include identifying ten facial expressions. Considering the increasing use of deep learning in recent years, in this study, using the convolution networks and, most importantly, using the concept of transfer learning, led us to use pre-trained networks to train our networks. Results: One way to improve accuracy in working with less counted data and deep-learning is to use pre-trained using pre-trained networks. Due to the small number of data sets, we used the techniques for data augmentation and eventually tripled the data size. These techniques include: rotating 10 degrees to the left and right and eventually turning to elastic transmation. We also applied deep Res-Net's network to public data sets existing for face expression by data augmentation. Conclusion: We saw a seven percent increase in accuracy compared to the highest accuracy in previous work on the considering dataset.

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 A Prediction of Emotions for Recognition of Facial Expressions using Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1076-1079
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Expression Recognition ◽  
Learning Approaches ◽  
Basic Emotions ◽  
Recognition Of Facial Expressions ◽  
Network Method ◽  
Machine Interface

Automated facial expression recognition can greatly improve the human–machine interface. Many deep learning approaches have been applied in recent years due to their outstanding recognition accuracy after training with large amounts of data. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some pre processing methods to show the influences of its in CNN performance. The preprocessing methods are :resizing, mean, normalization, standard deviation, scaling and edge detection . Face detection as single pre-processing phase achieved significant result with 100 % of accuracy, compared with another pre-processing phase and raw data.

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%.

Facial Action Recognition in 2D and 3D

2014 ◽  
pp. 167-186 ◽  
Author(s):  
Michel Valstar ◽  
Stefanos Zafeiriou ◽  
Maja Pantic
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Coding System ◽  
Recording Equipment ◽  
Facial Action ◽  
Facial Expression Analysis ◽  
Challenges And Opportunities ◽  
Almost All ◽  
2D And 3D

Automatic Facial Expression Analysis systems have come a long way since the earliest approaches in the early 1970s. We are now at a point where the first systems are commercially applied, most notably smile detectors included in digital cameras. As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has received significant and sustained attention within the field. Over the past 30 years, psychologists and neuroscientists have conducted extensive research on various aspects of human behaviour using facial expression analysis coded in terms of FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Mainly due to the cost effectiveness of existing recording equipment, until recently almost all work conducted in this area involves 2D imagery, despite their inherent problems relating to pose and illumination variations. In order to deal with these problems, 3D recordings are increasingly used in expression analysis research. In this chapter, the authors give an overview of 2D and 3D FACS recognition, and summarise current challenges and opportunities.

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