A systematic review on emotion recognition by using machine learning approaches (original) (raw)
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Human Emotion Recognition using Machine Learning
2019
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Publ...
Survey on AI-Based Multimodal Methods for Emotion Detection
Lecture Notes in Computer Science
Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.
Machine learning for human emotion recognition: a comprehensive review
2024
Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human-robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work. Keywords Emotion recognition analysis Á Physical signals Á Intrusive and non-intrusive emotion recognition Á Physiological signals Á Facial expressions Á Speech stimuli Á Body postures and gestures Á Machine learning and deep learning techniques
Techniques in Emotion Classi ication: An Overview
2022
Emotion classification i s a n e ssential t ask i n n atural l anguage p rocessing (NLP), a llowing systems to comprehend and respond to human emotions. This paper provides an overview of various techniques used in emotion classification, r a nging f r om t r aditional m e thods t o a d vanced Machine Learning models. I explore lexicon-based approaches, machine learning classifiers, d e ep learning methods, and the role of emojis in enhancing emotional understanding.
Negative emotions (anxiety, fear, anger, and grief) may affect physical health and the quality of life. Indeed, people with depression experience severe and prolonged feelings of negative emotions like sadness , anger, disgust and fear. On one hand, this paper presents a new method for the fusion of signals for the purpose of a multimodal recognition of eight basic emotions, on the other hand, it present a classification of these basic emotions in three emotional classes, namely, neutral, positive and negative emotions which are using physiological signals. After constructing an emotion data base during the learning phase, we apply the recognition algorithm on each modality separately. Then, we merge all these decisions separately by applying a decision fusion approach to improve recognition rate. The experiments show that the proposed method allows high accuracy emotion recognition. Indeed, we get a recognition rate of 81.69% under some conditions.
Emotion Detection: Comparison of Various Techniques
International Journal of Innovative Research in Computer Science & Technology
Expressions and body language can tell us a lot about what people are thinking. They are a form of non-verbal communication which tells us about how the person is feeling. It describes the mood of the person like whether he is happy or sad. This detection can be done using various techniques which are already based in the research papers like instrumented sensor technology and computer vision. It means that the expressions can be classified under different techniques like whether motion of the person is still or he is moving. This paper focuses on detecting the emotions of the person using computer vision. Using the Artificial Intelligence Technique and Mediapipe along with Computer Vision we are focusing on various joints in our body and storing their coordinates in a python file created there and then testing our Algorithm to detect the mood of the person. In addition, a dialogue box also pops us while detecting the emotions which tells us about the probability i.e the accuracy of...
Detection and Recognition of Human Emotion using Machine Learning
International Journal of Scientific Research in Science and Technology, 2023
This paper describes an emotion detection system based on real-time detection using image processing with human-friendly machine interaction. Facial detection has been around for decades. Taking a step ahead, human expressions displayed by face and felt by the brain, captured via video, electric signal, or image form can be approximated. To recognize emotions via images or videos is a difficult task for the human eye and challenging for machines thus detection of emotion by a machine requires many image processing techniques for feature extraction. This paper proposes a system that has two main processes such as face detection and facial expression recognition (FER). This research focuses on an experimental study on identifying facial emotions. The flow for an emotion detection system includes the image acquisition, preprocessing of an image, face detection, feature extraction, and classification. To identify such emotions, the emotion detection system uses KNN Classifier for image classification, and Haar cascade algorithm an Object Detection Algorithm to identify faces in an image or a real-time video. This system works by taking live images from the webcam. The objective of this research is to produce an automatic facial emotion detection system to identify different emotions based on these experiments the system could identify several people that are sad, surprised, and happy, in fear, are angry, etc.
Survey of various approaches of emotion detection via multimodal approach
Emotion detection of users is a challenging and exciting field where user's data is analysed to recognise emotions such as happy, sad, angry etc. This data could be in one or multiple formats such as audio, video ,text ,still images et al. Relevant features are extracted and fused together to give a label. Fusing data from two or more sources(modalities) is another challenge, feature level or decision level fusion is employed. This paper inspects and studies the various approaches to multimodal extraction of emotions.
EMOTION DETECTION USING MACHINE LEARNING
IJCSMC, 2019
Programmed Speech feeling acknowledgment has been a consuming issue since a decade ago, analysts have been endeavouring to build up a framework progressively like human, for feeling acknowledgment. Discourse has numerous parameters which have extraordinary weightage in perceiving feeling to be specific prosodic and spectral highlights, out of prosodic highlights to be specific pitch, intensity and energy are famously utilized and out of spectral highlights formant Mel Frequency Cepstral Coefficients are normally utilized by scientists around the world. Further the classifiers are prepared by utilizing these highlights for ordering feelings precisely, this venture is an endeavour to improve the existing innovation to achieve higher exactness and more extensive scope of feeling acknowledgment from discourse utilizing idea of Mel-recurrence Cepstrum Coefficients (MFCC) in the Python (Jupyter Notebook) An effective feeling acknowledgment framework can be valuable in the field of restorative science, mechanical autonomy building, call focus application and so forth.
Emotion Recognition through AI
Chapter 1: Introduction 1.1 Background and Relevance Emotions are central elements of human experience and communication 1. They influence not only our individual decisions and behaviors but also our interactions with others 2. Traditionally, the recognition and interpretation of emotions have been the domain of human intuition and psychological expertise 3. However, with the advent of Artificial Intelligence (AI) and machine learning, new possibilities have emerged to systematically and automatically recognize and analyze emotions 4. The ability of machines to recognize emotions has the potential to revolutionize a wide range of applications, from healthcare to education, marketing, and customer service 5. Emotional intelligence-the ability to recognize, understand, and influence one's own and others' emotions-is a key factor in successful human interactions 6. If machines can learn to recognize and respond to emotions, they could be capable of making more Emotion Recognition through AI Milaim Delija Researchers ID 0009-0005-2794-302X (ORCID) 3 Chapter 2 addresses the fundamentals of emotion recognition and provides an introduction to the significance of emotions as well as the methods for their recognition. Historical developments and current approaches are equally considered 12. Chapter 3 introduces the technological foundations of AI-based emotion recognition 13. The key algorithms and technologies, such as machine learning, neural networks, and multimodal recognition, are described in detail 14. Chapter 4 focuses on the practical applications of AI-based emotion recognition 15. Specific use cases from various fields such as healthcare, education, marketing, and security systems are presented 16. Chapter 5 examines the challenges and ethical implications associated with AI-based emotion recognition 17. Issues such as data privacy, bias in AI models, and societal acceptance are thoroughly discussed 18. Chapter 6 presents case studies and practical examples that demonstrate the concrete possibilities and successes of AI-based emotion recognition 19. Chapter 7 offers a look at future developments and trends in this field 20. It discusses how the technologies could evolve and what new application areas might be considered 21. Chapter 8 summarizes the key findings of the study and provides an outlook on future research topics and the practical importance of AI-based emotion recognition 22 .