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.

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.

scholarly journals Deep Capsule Network for Facial Emotion Recognition

2021 ◽  
pp. 1-6
Author(s):  
Tegani Salem ◽  
Telli Abdelmoutia
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Gradient Boosting ◽  
Expression Recognition ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Artificial Neural Network Ann ◽  
Classification Of Images

Although the classification of images has become one of the most important challenges, neural networks have had the most success with this task; this has shifted the focus towards architecture-based engineering rather than feature engineering. However, the enormous success of the convolutional neural network (CNN) is still far from comparable to the human brain's performance. In this context, a new and promising algorithm called a capsule net that is based on dynamic routing and activity vectors between capsules appeared as an efficient technique to exceed the limitations of the artificial neural network (ANN), which is considered to be one of the most important existing classifiers. This paper presents a new method-based capsule network with light-gradient-boosting-machine (LightGBM) classifiers for facial emotion recognition. To achieve our aim, there were two steps to our technique. Initially, the capsule networks were merely employed for feature extraction. Then, using the outputs computed from the capsule networks, a LightGBM classifier was utilised to detect seven fundamental facial expressions. Experiments were carried out to evaluate the suggested facial-expression-recognition system's performance. The efficacy of our proposed method, which achieved an accuracy rate of 91%, was proven by its testing the results on the CK+ dataset. KEYWORDS Image classifications, LightGBM, machine learning, computer vision, CNN, deep learning

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 Real-time facial emotion recognition using lightweight convolution neural network

2021 ◽  
Vol 1827 (1) ◽  
pp. 012130
Author(s):  
Qi Li ◽  
Yun Qing Liu ◽  
Yue Qi Peng ◽  
Cong Liu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Convolution Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion

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.

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.

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