Facial expression emotion recognition is an intuitive reflection of a person’s mental state, which contains rich emotional information, and is one of the most important forms of interpersonal communication. Facial expression emotion recognition does the task of classifying the expressions on facial images into various categories such as anger, fear, surprise, sadness, happiness and so on. It analyses facial expressions from both static images and videos in order to reveal information on one’s emotional state. FER analysis comprises three steps: a) face detection, b) facial expression detection, and c) expression classification to an emotional state. Emotion detection is based on the analysis of facial landmark positions (e.g. end of the nose, eyebrows). Furthermore, in videos, changes in those positions are also analysed, in order to identify contractions in a group of facial muscles.

• Emotion Recognition
• Facial Analysis
• Video Emotion Analysis

Facial expression emotion recognition using OpenCV is a technology that classifies emotions (such as anger, happiness) based on facial features in images and videos. It involves three key steps: face detection, tracking facial landmarks, and identifying muscle contractions to determine emotions. This technology finds applications in human-computer interaction and sentiment analysis, demonstrating the powerful potential of OpenCV in understanding and interpreting human emotions, enriching the scope of human-machine interfaces and psychological research.

• Emotion Recognition
• OpenCV