Divyanshu-hash/Real-Time-Emotion-Detection
Real-Time Emotion Detection using MobileNetV2 is a deep learning project that detects and classifies human emotions in real time via webcam. It uses a MobileNetV2 model trained on facial expressions and OpenCV for face detection to recognize emotions like Happy, Sad, Angry, and more.
This tool helps you instantly recognize human emotions from live webcam video. It takes a real-time video feed of faces and identifies whether a person appears angry, sad, happy, neutral, fearful, surprised, or disgusted. This is ideal for researchers studying human behavior, user experience professionals assessing reactions, or anyone needing immediate emotional insights from live video.
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Use this if you need to quickly and automatically detect the dominant emotion on a person's face from a live webcam feed.
Not ideal if you need to analyze complex emotional nuances, detect emotions from pre-recorded video files, or work in low-light conditions.
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MIT
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Last pushed
Jul 31, 2025
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