scholarly journals Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1897 ◽  
Author(s):  
Dhwani Mehta ◽  
Mohammad Faridul Haque Siddiqui ◽  
Ahmad Y. Javaid
Keyword(s):  
Comparative Study ◽  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Support Vector ◽  
Human Machine Interaction ◽  
Intensity Estimation ◽  
Intensity Of Emotion

Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.

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.

scholarly journals Facial Emotion Recognition Using Hybrid Features

Informatics ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Abdulrahman Alreshidi ◽  
Mohib Ullah
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Skin Color ◽  
Modular Design ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Hybrid Features ◽  
Cascade Classifiers ◽  
Reference Methods

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network

2019 ◽  
Vol 23 (5) ◽  
pp. 883-890 ◽  
Author(s):  
Xiaobo Liu ◽  
Xu Yin ◽  
Min Wang ◽  
Yaoming Cai ◽  
Guang Qi ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Complex Structure ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Model Complexity ◽  
Psychological State ◽  
Data Sets ◽  
Human Machine Interaction ◽  
Convolutional Network ◽  
Deep Forest

In human-machine interaction, facial emotion recognition plays an important role in recognizing the psychological state of humans. In this study, we propose a novel emotion recognition framework based on using a knowledge transfer approach to capture features and employ an improved deep forest model to determine the final emotion types. The structure of a very deep convolutional network is learned from ImageNet and is utilized to extract face and emotion features from other data sets, solving the problem of insufficiently labeled samples. Then, these features are input into a classifier called multi-composition deep forest, which consists of 16 types of forests for facial emotion recognition, to enhance the diversity of the framework. The proposed method does not need require to train a network with a complex structure, and the decision tree-based classifier can achieve accurate results with very few parameters, making it easier to implement, train, and apply in practice. Moreover, the classifier can adaptively decide its model complexity without iteratively updating parameters. The experimental results for two emotion recognition problems demonstrate the superiority of the proposed method over several well-known methods in facial emotion recognition.

scholarly journals Recognition of Face Emotion using Convolutional Neural Network

2020 ◽  
Vol 9 (9) ◽  
pp. 364-376
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Recognition System ◽  
Video Stream ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.

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