Facial Expression Analysis Using Deep Learning With Partial Integration To Other Modalities To Detect Emotion
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Facial Expression Analysis Using Deep Learning With Partial Integration To Other Modalities To Detect Emotion
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Author : Mehdi Ghayoumi
language : en
Publisher:
Release Date : 2017
Facial Expression Analysis Using Deep Learning With Partial Integration To Other Modalities To Detect Emotion written by Mehdi Ghayoumi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Analysis of human emotion is very important as the field of social robotics where a new generation of humanoids and other smart devices will interact with humans. Emotional expression is a universal language for interaction with humans. Understanding human emotions is a necessary and important step for human-computer interaction. Human emotion is expressed as a complex combination of facial expressions, speech (including silence) and gestures postures, various limb-motions, gaze, and blinking. Multiple research models have been developed for limited facial expression analysis, speech based emotion analysis, limited models for gesture analysis and their limited integration. However, such analysis is limited to single frame analysis time-efficiency, limited handling of occlusion, notion of colors in facial expression analysis, lack of exploitation of symmetry, lack of dynamic change in assigning weight between the modalities based upon environmental requirement and six basic emotions.This research develops a convolutional neural network based deep learning model that recognizes human facial expressions exploiting a combination of symmetrical representation to handle occlusion; a unified model based upon transforming facial muscle motion to geometric feature points; fusion of multiple modalities and fast hashing techniques for real-time emotion recognition. It also proposes a new model for recognition of mixed-emotion in real-time.
Emotion Recognition
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Author : Amit Konar
language : en
Publisher: John Wiley & Sons
Release Date : 2015-01-27
Emotion Recognition written by Amit Konar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-27 with Technology & Engineering categories.
A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
Machine And Deep Learning Techniques For Emotion Detection
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Author : Mritunjay Rai
language : en
Publisher: Medical Information Science Reference
Release Date : 2024-03-22
Machine And Deep Learning Techniques For Emotion Detection written by Mritunjay Rai and has been published by Medical Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers 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. Focusing on addressing challenges such as cross-cultural variability, data privacy, and model interpretability, Machine and Deep Learning Techniques for Emotion Detection provide a holistic perspective on the ethical, legal, and societal implications of deploying emotion detection technologies. Whether readers are researchers exploring convolutional neural networks for facial expression analysis or practitioners integrating emotion detection into healthcare or marketing, this book provides a comprehensive guide for unlocking the transformative potential of this burgeoning field.
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).
Deep Learning In Practice
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Author : Mehdi Ghayoumi
language : en
Publisher: CRC Press
Release Date : 2021-12-01
Deep Learning In Practice written by Mehdi Ghayoumi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-01 with Business & Economics categories.
Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.
A Novel Deep Learning Approach For Emotion Classification
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Author : Satya Chandrashekhar Ayyalasomayajula
language : en
Publisher:
Release Date : 2022
A Novel Deep Learning Approach For Emotion Classification written by Satya Chandrashekhar Ayyalasomayajula 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.
Neural Networks are at the core of computer vision solutions for various applications. With the advent of deep neural networks Facial Expression Recognition (FER) has been a very ineluctable and challenging task in the field of computer vision. Micro-expressions (ME) have been quite prominently used in security, psychotherapy, neuroscience and have a wide role in several related disciplines. However, due to the subtle movements of facial muscles, the micro-expressions are difficult to detect and identify. Due to the above, emotion detection and classification have always been hot research topics. The recently adopted networks to train FERs are yet to focus on issues caused due to overfitting, effectuated by insufficient data for training and expression unrelated variations like gender bias, face occlusions and others. Association of FER with the Speech Emotion Recognition (SER) triggered the development of multimodal neural networks for emotion classification in which the application of sensors played a significant role as they substantially increased the accuracy by providing high quality inputs, further elevating the efficiency of the system. This thesis relates to the exploration of different principles behind application of deep neural networks with a strong focus towards Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) in regards to their applications to emotion recognition. A Motion Magnification algorithm for ME's detection and classification was implemented for applications requiring near real-time computations. A new and improved architecture using a Multimodal Network was implemented. In addition to the motion magnification technique for emotion classification and extraction, the Multimodal algorithm takes the audio-visual cues as inputs and reads the MEs on the real face of the participant. This feature of the above architecture can be deployed while administering interviews, or supervising ICU patients in hospitals, in the auto industry, and many others. The real-time emotion classifier based on state-of-the-art Image-Avatar Animation model was tested on simulated subjects. The salient features of the real-face are mapped on avatars that are build with a 3D scene generation platform. In pursuit of the goal of emotion classification, the Image Animation model outperforms all baselines and prior works. Extensive tests and results obtained demonstrate the validity of the approach.
Facial Analytics For Emotional State Recognition
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Author : Konstantinos Papazachariou
language : en
Publisher:
Release Date : 2017
Facial Analytics For Emotional State Recognition written by Konstantinos Papazachariou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
For more than 75 years, social scientists study the human emotions. Whereas numerous theories developed about the provenance and number of basic emotions, most agreed that they could categorize into six categories: angrer, disgust, fear, joy, sadness and surprise. To evaluate emotions, psychologists focused their research in facial expressions analysis. In recent years, the progress in digital technologies field has steered the researchers in psychology, computer science, linguistics, neuroscience, and related disciplines towards the usage of computer systems that analyze and detect the human emotions. Usually, these algorithms are referred in the literature as facial emotion recognition (FER) systems. In this thesis, two different approaches are described and evaluated in order to recognize the six basic emotions automatically from still images.An effective face detection scheme, based on color techniques and the well-known Viola and Jones (VJ) algorithm is proposed for the face and facial characteristics localization within an image. A novel algorithm which exploits the eyes' centers coordinates, is applied on the image to align the detected face. In order to reduce the effects of illumination, homomorphic filtering is applied on the face area. Three regions (mouth, eyes and glabella) are localized and further processed for texture analysis.Although many methods have been proposed in the literature to recognize the emotion from the human face, they are not designed to be able to handle partial occlusions and multiple faces. Therefore, a novel algorithm that extracts information through texture analysis, from each region of interest, is evaluated. Two popular techniques (histograms of oriented gradients and local binary patterns) are utilized to perform texture analysis in the abovementioned facial patches. By evaluating several combinations of their principal parameters and two classification techniques (support vector machine and linear discriminant analysis), three classifiers are proposed. These three models are enabled depending on the regions' availability. Although both classification approaches have shown impressive results, LDA proved to be slightly better especially regarding the amount of data management. Therefore, the final models, which utilized for comparison purpose, were trained using LDA classification.Experiments using Cohn-Kanade plus (CK+) and Amsterdam Dynamic Facial Expression Set (ADFES) datasets demonstrate that the presented FER algorithm has surpassed other significant FER systems in terms of processing time and accuracy. The evaluation of the system involved three experiments: intra-testing experiment (train and test with the same dataset), train/test process between CK+ and ADFES and finally the development of a new database based on selfie-photos, which is tested on the pre-trained models. The last two experiments constitute a certain evidence that Emotion Recognition System (ERS) can operate under various pose and light circumstances.
The Oxford Handbook Of Affective Computing
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Author : Rafael A. Calvo
language : en
Publisher: Oxford Library of Psychology
Release Date : 2015
The Oxford Handbook Of Affective Computing written by Rafael A. Calvo and has been published by Oxford Library of Psychology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computers categories.
"The Oxford Handbook of Affective Computing is a definitive reference in the burgeoning field of affective computing (AC), a multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and other disciplines. AC research explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, and on the design, implementation, and evaluation of systems involving affect at their core. The volume features 41 chapters and is divided into five sections: history and theory, detection, generation, methodologies, and applications. Section 1 begins with the making of AC and a historical review of the science of emotion. The following chapters discuss the theoretical underpinnings of AC from an interdisciplinary viewpoint. Section 2 examines affect detection or recognition, a commonly investigated area. Section 3 focuses on aspects of affect generation, including the synthesis of emotion and its expression via facial features, speech, postures, and gestures. Cultural issues are also discussed. Section 4 focuses on methodological issues in AC research, including data collection techniques, multimodal affect databases, formats for the representation of emotion, crowdsourcing techniques, machine learning approaches, affect elicitation techniques, useful AC tools, and ethical issues. Finally, Section 5 highlights applications of AC in such domains as formal and informal learning, games, robotics, virtual reality, autism research, health care, cyberpsychology, music, deception, reflective writing, and cyberpsychology. This compendium will prove suitable for use as a textbook and serve as a valuable resource for everyone with an interest in AC."--
Human Emotion Recognition From Face Images
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Author : Asit Barman
language : en
Publisher: Independent Author
Release Date : 2023-05-31
Human Emotion Recognition From Face Images written by Asit Barman and has been published by Independent Author this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with categories.
When it comes to recognizing facial emotion, the distance feature is extremely important. In the field of affective computing, identifying accurate landmarks is critical as well as a difficult issue. The appearance model detects prominent landmarks on human faces. On the human face, these prominent landmarks form a grid. Distances are calculated within the grid by comparing one landmark point to another. Normalized distances are regarded as a distance signature. To form a normalized shape signature, the possible triangles are found within the grid. Texture characteristics among the landmark points reflected in human faces are important features in facial expression recognition. Appearance-based models detect effective landmarks, and corresponding texture regions are extracted from face images. The texture feature is computed using a Local Binary Pattern (LBP). Normalizing texture signatures is accomplished with the texture feature. A novel concept of corresponding stability indices is introduced, which are eventually discovered to play an important role in facial expression recognition. For these reasons, the stability indices are calculated from each normalized distance, shape, and texture signature feature. To supplement the feature set, individual distance, shape, and texture signature features are used to calculate statistical analyses such as range, moment, skewness, kurtosis, and entropy. The enhanced distance signature feature set is fed into a Multilayer Perceptron (MLP) to generate various expression categories such as anger, sadness, fear, disgust, surprise, and happiness. We train and test our proposed system on four benchmark datasets: Cohn-Kanade (CK+), JAFFE, MMI, and MUG. To categories the expressions, the shape signature feature set is fed into Multilayer Perceptron (MLP) and Nonlinear Auto Regressive with eXogenous (NARX). We tested our proposed system on four databases and found that it outperformed other state-of-the-art solutions. To conduct the experiments, the texture signature feature is used as an input to Nonlinear Auto Regressive with eXogenous (NARX) for recognition of human facial expressions on benchmark datasets, and the results support the effectiveness of the proposed procedure. Following the recognition of expressions using individual signature features, we investigate the combined distance and shape (D-S), distance and texture (D-T), and shape and texture (S-T) signature features. To conduct and validate our experiment and establish its performance superiority over other existing competitors, we feed the combined distance and shape (D-S) feature set into a Multilayer Perceptron (MLP) to categorize the expressions into different categories on four databases. The combined distance-texture (D-T) signature outperforms the distance and texture signatures separately. The effectiveness of the proposed technique based on combined D-T signature is demonstrated by its extremely encouraging performance when compared to other existing arts. To classify the expression on the CK+, JAFFE, MMI, MUG, and Wild face benchmark databases, the combined shape and texture (S-T) features are fed into Multilayer Perceptron (MLP) and Deep Belief Neural (DBN) networks. Extensive testing demonstrates that our proposed methodology outperforms other existing competitors in terms of performance. Finally, the distance signature, shape signature, and texture signature are combined to form a distance-shape-texture signature trio feature for recognizing facial expression. The experimental results also show a promising recognition rate of facial expressions of the distance-shape-texture signature trio when compared to other existing arts.
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.