scholarly journals A Review on Facial Emotion Recognition Using Machine and Deep Learning Algorithm

2020 ◽  
pp. 09-13
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Learning Algorithm ◽  
Facial Feature ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Expression Recognition

Facial emotions are the changes in facial expressions about a person’s inner excited tempers, objectives, or social exchanges which are scrutinized with the aid of computer structures that attempt to subsequently inspect and identify the facial feature and movement variations from visual data. Facial emotion recognition (FER) is a noteworthy area in the arena of computer vision and artificial intelligence due to its significant commercial and academic potential. FER has become a widespread concept of deep learning and offers more fields for application in our day-to-day life. Facial expression recognition (FER) has gathered widespread consideration recently as facial expressions are thought of as the fastest medium for communicating any of any sort of information. Recognizing facial expressions provides an improved understanding of a person’s thoughts or views. With the latest improvement in computer vision and machine learning, it is plausible to identify emotions from images. Analyzing them with the presently emerging deep learning methods enhance the accuracy rate tremendously as compared to the traditional contemporary systems. This paper emphases the review of a few of the machine learning, deep learning, and transfer learning techniques used by several researchers that flagged the means to advance the classification accurateness of the FEM.

scholarly journals Intelligent Facial Emotion Recognition

2019 ◽  
pp. 486-495
Author(s):  
Shahana A. ◽  
Harish Binu K. P.
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Facial Feature ◽  
Local Binary Patterns ◽  
Small Population ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Expression Recognition ◽  
Search Mechanism ◽  
Microgenetic Algorithm

The system introduces an intelligent facial emotion recognition using arti?cial neural network (ANN). The concept ?rst takes modi?ed local binary patterns, which involve horizontal vertical and neighborhood pixel comparison, to produce initial facial representation. Then, a microgenetic algorithm(mGA) embedded Particle Swarm Optimization(PSO), is proposed for feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a sub dimension based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Arti?cial Neural Network is used as a classi?er for recognizing seven facial emotions. ANN is implemented as classi?er for pattern recognition. Based on a comprehensive study using within- and cross-domain images from the extended Japanese database. The empirical results indicate that our proposed system outperforms other state of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

scholarly journals Negative parental emotional environment increases the association between childhood behavioral problems and impaired recognition of negative facial expressions

2021 ◽  
pp. 1-10
Author(s):  
Daniel T. Burley ◽  
Christopher W. Hobson ◽  
Dolapo Adegboye ◽  
Katherine H. Shelton ◽  
Stephanie H.M. van Goozen
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Behavioral Problems ◽  
Expressed Emotion ◽  
Interactive Effect ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Environment ◽  
Negative Comments ◽  
Child Behavioral Problems

Abstract Impaired facial emotion recognition is a transdiagnostic risk factor for a range of psychiatric disorders. Childhood behavioral difficulties and parental emotional environment have been independently associated with impaired emotion recognition; however, no study has examined the contribution of these factors in conjunction. We measured recognition of negative (sad, fear, anger), neutral, and happy facial expressions in 135 children aged 5–7 years referred by their teachers for behavioral problems. Parental emotional environment was assessed for parental expressed emotion (EE) – characterized by negative comments, reduced positive comments, low warmth, and negativity towards their child – using the 5-minute speech sample. Child behavioral problems were measured using the teacher-informant Strengths and Difficulties Questionnaire (SDQ). Child behavioral problems and parental EE were independently associated with impaired recognition of negative facial expressions specifically. An interactive effect revealed that the combination of both factors was associated with the greatest risk for impaired recognition of negative faces, and in particular sad facial expressions. No relationships emerged for the identification of happy facial expressions. This study furthers our understanding of multidimensional processes associated with the development of facial emotion recognition and supports the importance of early interventions that target this domain.

scholarly journals Effects of early institutionalization on emotion processing in 12-year-old youth

2017 ◽  
Vol 29 (5) ◽  
pp. 1749-1761 ◽  
Author(s):  
Johanna Bick ◽  
Rhiannon Luyster ◽  
Nathan A. Fox ◽  
Charles H. Zeanah ◽  
Charles A. Nelson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Positive Impact ◽  
Developmental Trajectories ◽  
Emotion Processing ◽  
Institutional Care ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Facial Expressions ◽  
Fearful Faces

AbstractWe examined facial emotion recognition in 12-year-olds in a longitudinally followed sample of children with and without exposure to early life psychosocial deprivation (institutional care). Half of the institutionally reared children were randomized into foster care homes during the first years of life. Facial emotion recognition was examined in a behavioral task using morphed images. This same task had been administered when children were 8 years old. Neutral facial expressions were morphed with happy, sad, angry, and fearful emotional facial expressions, and children were asked to identify the emotion of each face, which varied in intensity. Consistent with our previous report, we show that some areas of emotion processing, involving the recognition of happy and fearful faces, are affected by early deprivation, whereas other areas, involving the recognition of sad and angry faces, appear to be unaffected. We also show that early intervention can have a lasting positive impact, normalizing developmental trajectories of processing negative emotions (fear) into the late childhood/preadolescent period.

Deep Learning Model for Facial Emotion Recognition

2019 ◽  
pp. 543-558 ◽  
Author(s):  
Ajeet Ram Pathak ◽  
Somesh Bhalsing ◽  
Shivani Desai ◽  
Monica Gandhi ◽  
Pranathi Patwardhan
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Learning Model ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Deep Learning Model

scholarly journals Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

2021 ◽  
Author(s):  
Naveen Kumari ◽  
Rekha Bhatia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Models ◽  
Recognition Model ◽  
Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

scholarly journals Review: Posed vs. Genuine Facial Emotion Recognition and Expression in Autism and Implications for Intervention

2021 ◽  
Vol 12 ◽  
Author(s):  
Paula J. Webster ◽  
Shuo Wang ◽  
Xin Li
Keyword(s):  
Social Interaction ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Autism Spectrum ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Dynamic Stimuli ◽  
Behavioral Studies ◽  
Facial Expressions Of Emotion ◽  
Cartoon Characters

Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER – namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

Traditional versus Neural Network Classification Methods for Facial Emotion Recognition

2021 ◽  
Vol 7 (2) ◽  
pp. 203-206
Author(s):  
Herag Arabian ◽  
Verena Wagner-Hartl ◽  
Knut Moeller
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Emotion Recognition ◽  
Network Architecture ◽  
Autism Spectrum ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Model

Abstract Facial emotion recognition (FER) is a topic that has gained interest over the years for its role in bridging the gap between Human and Machine interactions. This study explores the potential of real time FER modelling, to be integrated in a closed loop system, to help in treatment of children suffering from Autism Spectrum Disorder (ASD). The aim of this study is to show the differences between implementing Traditional machine learning and Deep learning approaches for FER modelling. Two classification approaches were taken, the first approach was based on classic machine learning techniques using Histogram of Oriented Gradients (HOG) for feature extraction, with a k-Nearest Neighbor and a Support Vector Machine model as classifiers. The second approach uses Transfer Learning based on the popular “Alex Net” Neural Network architecture. The performance of the approaches was based on the accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. The data analyzed shows that traditional machine learning methods are as effective as deep neural net models and are a good compromise between accuracy, extracted features, computational speed and costs.

Real-time facial expression recognition using smoothed deep neural network ensemble

2020 ◽  
Vol 28 (1) ◽  
pp. 97-111
Author(s):  
Nadir Kamel Benamara ◽  
Mikel Val-Calvo ◽  
Jose Ramón Álvarez-Sánchez ◽  
Alejandro Díaz-Morcillo ◽  
Jose Manuel Ferrández-Vicente ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Facial Emotion Recognition ◽  
Training Data ◽  
Facial Emotion ◽  
Processing Unit ◽  
Expression Recognition ◽  
Neural Network Ensemble

Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.

Sign in / Sign up

Export Citation Format

Share Document