Emotion Detection Using Deep Learning Techniques

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Emotion Detection Using Deep Learning Techniques

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Author : Syyada Shumaila Khurshid
language : en
Publisher: Independently Published
Release Date : 2024-10-19

Emotion Detection Using Deep Learning Techniques written by Syyada Shumaila Khurshid and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-19 with Computers categories.

Determining human emotions from photographs is a difficult but important challenge for social communication. Emotion detection using traditional approaches is typically inefficient and inaccurate. In this study, we investigate how convolutional neural networks (CNNs), a type of deep learning technique, may improve the ability to identify emotions from facial expressions. In order to increase CNN efficacy, we test several preprocessing methods and refine CNN designs to identify eight fundamental emotions. Our goal is to improve human emotion recognition and classification through deep learning, so that computers can react to human emotions and behaviors more precisely. The research dataset consists of roughly 32,290 photos with various expressions on their faces. Our approach includes preprocessing processes like feature extraction and noise reduction to improve image quality. To reliably classify facial expressions, we present an enhanced CNN (ECNN) technique that is in line with the Facial Action Coding System (FACS). We test our ECNN model empirically and compare its performance to that of conventional CNNs and support vector machines (SVMs). The results show that our ECNN methodology achieves higher accuracy rates in emotion categorization than previous methods. We show notable gains in computing efficiency and classification performance by utilizing deep learning techniques. Our research advances face expression recognition technology, which has ramifications for a number of fields including social robots, affective computing, and human-computer interaction.

Machine And Deep Learning Techniques For Emotion Detection

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Author : Rai, Mritunjay
language : en
Publisher: IGI Global
Release Date : 2024-05-14

Machine And Deep Learning Techniques For Emotion Detection written by Rai, Mritunjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Psychology categories.

Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.

Deep Learning Techniques Applied To Affective Computing

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Author : Zhen Cui
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-14

Deep Learning Techniques Applied To Affective Computing written by Zhen Cui and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Science categories.

Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.

Recent Advances In Machine Learning Techniques And Sensor Applications For Human Emotion Activity Recognition And Support

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Author : Kyandoghere Kyamakya
language : en
Publisher: Springer Nature
Release Date : 2024-11-07

Recent Advances In Machine Learning Techniques And Sensor Applications For Human Emotion Activity Recognition And Support written by Kyandoghere Kyamakya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-07 with Computers categories.

This book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. It covers state-of-the-art machine learning techniques and large language models using multimodal sensors. Enhancing the quality of life for individuals with special needs, particularly the elderly, is a key focus in Active and Assisted Living (AAL) research. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems. This book offers insights into technologies transforming AAL for researchers, students, and practitioners. It is a valuable resource for developing responsive and personalized support systems that enhance life quality in smart environments. It is also essential for advancing the understanding of machine learning and sensor technologies in AAL and emotion recognition.

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies

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Author : Kyandoghere Kyamakya
language : en
Publisher: MDPI
Release Date : 2021-09-01

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies written by Kyandoghere Kyamakya and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Technology & Engineering categories.

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.

Facial Emotion Detection Using Deep Learning

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Author : Darren Green
language : en
Publisher:
Release Date : 2022

Facial Emotion Detection Using Deep Learning written by Darren Green and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Facial Expression Recognition (FER) has remained a difficult and fascinating issue. Despite the efforts put into establishing distinct FER methods, existing systems have usually lacked generalizability when applied to unseen photos or those recorded in a natural context. Modern artificial intelligence systems must be able to replicate and evaluate reactions from human faces, therefore Facial emotion recognition is critical. This can help you make better judgments, whether it's about detecting malicious intent, promoting deals, or avoiding security issues. Recognizing emotions from photos or video is a simple operation for the human eye, but it's a difficult challenge for automated systems, requiring a variety of image processing approaches. Therehas now been an increase in designing FER (Facial emotion recognition) systems within the realm of Machine Learning. We have seen an increase in the amount of research done towards it. Most conventional FER systems use typical Machine Learning methodologies to resolve this problem. However, these methods are not able to generalize optimally. In this project we attempt to make use of more recent methodologies which will categorize faces into specific facial emotion types. This will be achieved making use of Convolution Neural Networks (CNNs).

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies

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Author : Kyandoghere Kyamakya
language : en
Publisher:
Release Date : 2021

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies written by Kyandoghere Kyamakya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.

Multi Label Emotion Classification Using Machine Learning And Deep Learning Methods

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Author : Drashtikumari Kher
language : en
Publisher:
Release Date : 2021

Multi Label Emotion Classification Using Machine Learning And Deep Learning Methods written by Drashtikumari Kher and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Emotion detection in online social networks benefits many applications like personalized advertisement services, suggestion systems, etc. Emotion can be identified from various sources like text, facial expressions, images, speeches, paintings, songs, etc. Emotion detection can be done by various techniques in machine learning. Traditional emotion detection techniques mainly focus on multi-class classification while ignoring the co-existence of multiple emotion labels in one instance. This research work is focussed on classifying multiple emotions from data to handle complex data with the help of different machine learning and deep learning methods. Before modeling, first data analysis is done and then the data is cleaned. Data pre-processing is performed in steps such as stop-words removal, tokenization, stemming and lemmatization, etc., which are performed using a Natural Language Processing toolkit (NLTK). All the input variables are converted into vectors by naive text encoding techniques like word2vec, Bag-of-words, and term frequency-inverse document frequency (TF-IDF). This research is implemented using python programming language. To solve multi-label emotion classification problem, machine learning and deep learning methods were used. The evaluation parameters such as accuracy, precision, recall, and F1-score were used to evaluate the performance of the classifiers Naïve Bayes, support vector machine (SVM), Random Forest, K-nearest neighbour (KNN), GRU (Gated Recurrent Unit) based RNN (Recurrent Neural Network) with Adam optimizer and Rmsprop optimizer. GRU based RNN with Rmsprop optimizer achieves an accuracy of 82.3%, Naïve Bayes achieves highest precision of 0.80, Random Forest achieves highest recall score of 0.823, SVM achieves highest F1 score of 0.798 on the challenging SemEval2018 Task 1: E-c multi-label emotion classification dataset. Also, One-way Analysis of Variance (ANOVA) test was performed on the mean values of performance metrics (accuracy, precision, recall, and F1-score) on all the methods.

Emotion Artificial Intelligence As Improvement For E Learning During Covid 19

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Author : Hari K. C.
language : en
Publisher: GRIN Verlag
Release Date : 2020-10-05

Emotion Artificial Intelligence As Improvement For E Learning During Covid 19 written by Hari K. C. and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-05 with Computers categories.

Academic Paper from the year 2020 in the subject Computer Sciences - Artificial Intelligence, grade: 10.00, , course: Electronics and Computer, language: English, abstract: In this paper student’s emotions such as excitement, happiness, confusion, sadness, desire and surprise, will be analysed by an Emotion AI. Therefore the neural network model, designed to capture the facial expression, is used. Deep learning is the emerging techniques to process large datasets of images with Kera’s using TensorFlow backend. Convolution Neural Network is an artificial neural network that has specialization in detection and classification. Convolution neural network has hidden layers called convolution layers. This layer consists of neurons. Facial emotion recognition usually employs a training and testing stage to produce the desirable output. The emotion of the students plays the vital role to determine the student interest in attending classes. Facial expressions are among the most universal forms of body language. The facial expressions are almost similar throughout the world. The facial expression, movement of head, eye, mouth helps to identify the emotions of the students so that the level of interest of student can be predicted form the emotion analysis of students. For example: A smile can be used to indicate happiness. Facial expression reveals the true feelings about a situation. Then, after collecting those information, e-learning quality can be improved and enhanced. The reaction of the students is analyzed during the teaching and learning course. Thus, the mood of students can be predicted easily which help to improve the e - learning environment. The feedback will be provided to teachers to enhance the teaching and learning process in e-learning.

International Conference On Multimodal Interfaces

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Author :
language : en
Publisher:
Release Date : 2006

International Conference On Multimodal Interfaces written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Human-computer interaction categories.