Emotion recognition is the process of identifying people's emotions. The accuracy with which people would recognize the feelings of others varies considerably. The use of machine learning and artificial intelligence to assist people with emotion recognition is a relatively new research area. Since ancient times, researchers have been interested in automatically recognizing emotions. Currently, emotion recognition is done by identifying facial expressions in videos, analyzing speeches in audio content, and analyzing content on social media. Physiological signal measures, such as brain signals (EEG), ECG, body temperature, and artificial intelligence techniques, are emerging for emotion recognition.
Emotion recognition has applications in various industries, including retail, education, health care, and security. Marketing/advertising firms want to know the customers’ reactions to advertisements, design, and products by analyzing their emotions. Education applications measure students’ response, engagement, and interest in the content and develop personalized content by incorporating emotion as feedback. It can also play an essential role in security. It can recognize individuals for suspicious behavior in a crowd by tracking their current emotional state, age, and criminal record. Real-time emotion recognition can stop potential terrorist activity.
The purpose of this Research Topic is to present the recent advancement of Emotion recognition techniques in Artificial Intelligence, Machine Learning, and the medical science field to the broader scientific community.
The main topics include but are not limited to
1. AI-based Emotion Recognition Techniques.
2. Emotion Recognition with Machine Learning Algorithms.
3. Emotion Recognition with AI-based EEG Signal Classification.
4. Emotion Recognition with Facial Expression Classification using ML Algorithm.
5. Emotion Recognition with Physiological Signals, Classification using AI-ML techniques.
6. Image Processing based Emotion Recognition.
7. Digital Footprint-based Emotion Recognition using AI Techniques.
8. Emotion Recognition Classification using Deep Learning Algorithms.
9. Brain Signal Classification for Emotion Recognition with ML Algorithms
10. Techniques in Medical Science for Emotion Recognition
Emotion recognition is the process of identifying people's emotions. The accuracy with which people would recognize the feelings of others varies considerably. The use of machine learning and artificial intelligence to assist people with emotion recognition is a relatively new research area. Since ancient times, researchers have been interested in automatically recognizing emotions. Currently, emotion recognition is done by identifying facial expressions in videos, analyzing speeches in audio content, and analyzing content on social media. Physiological signal measures, such as brain signals (EEG), ECG, body temperature, and artificial intelligence techniques, are emerging for emotion recognition.
Emotion recognition has applications in various industries, including retail, education, health care, and security. Marketing/advertising firms want to know the customers’ reactions to advertisements, design, and products by analyzing their emotions. Education applications measure students’ response, engagement, and interest in the content and develop personalized content by incorporating emotion as feedback. It can also play an essential role in security. It can recognize individuals for suspicious behavior in a crowd by tracking their current emotional state, age, and criminal record. Real-time emotion recognition can stop potential terrorist activity.
The purpose of this Research Topic is to present the recent advancement of Emotion recognition techniques in Artificial Intelligence, Machine Learning, and the medical science field to the broader scientific community.
The main topics include but are not limited to
1. AI-based Emotion Recognition Techniques.
2. Emotion Recognition with Machine Learning Algorithms.
3. Emotion Recognition with AI-based EEG Signal Classification.
4. Emotion Recognition with Facial Expression Classification using ML Algorithm.
5. Emotion Recognition with Physiological Signals, Classification using AI-ML techniques.
6. Image Processing based Emotion Recognition.
7. Digital Footprint-based Emotion Recognition using AI Techniques.
8. Emotion Recognition Classification using Deep Learning Algorithms.
9. Brain Signal Classification for Emotion Recognition with ML Algorithms
10. Techniques in Medical Science for Emotion Recognition
