Real-Time Emotion Detection System: A Hybrid Approach of Computer Vision and Machine Learning Techniques

Facial expression-based emotion recognition is a fascinating research topic that has been presented and applied in a variety of domains, such as surveillance, mental health, and man–machine interactions. With the aid of the Emotion Detection System, we can determine a person's emotion and determine whether they are happy, sad, shocked, neutral, angry, etc. A happy society and a decrease in crime may result from knowing a person's emotional state. With the help of feature characteristics, our real-time emotion classification algorithm decodes face emotions to produce precise predictions. We employed a CNN-based model, which produced high accuracy, after implementing and comparing several classifiers, including SVM and ANN. About 35,000 photos were used in the model's training. Face localization, feature extraction, and emotion tagging are the three stages of the model's operation.