This project uses OpenCV and DeepFace to detect faces and analyze emotions in real-time from a webcam feed. The program captures video frames, detects faces, and overlays the detected emotions on the video.

• 🧑‍🤝‍🧑 Face Detection: Detects faces using OpenCV's Haar Cascade Classifier.
• 😄 Emotion Analysis: Uses DeepFace to analyze emotions from the detected faces.
• 🎥 Real-Time Video Feed: Displays live webcam feed with detected emotions.
• 🖥️ Simple Interface: Real-time feedback with emotion labels on the video.

• Programming Language: Python
• Face Detection: OpenCV (Haar Cascade Classifier)
• Emotion Recognition: DeepFace
• Computer Vision Library: OpenCV
• Webcam Access: OpenCV VideoCapture

1. Clone the Repository

   git clone https://github.com/your-username/face-emotion-detection.git
   cd face-emotion-detection

2. Install Dependencies

   • Install required Python libraries:

   pip install opencv-python deepface

3. Run the Application

   python face2.py

   • The program will start capturing video from the webcam, and display the dominant emotion detected from the face in real-time.

• Face Detection: Uses OpenCV's pre-trained Haar Cascade classifier to detect faces in the video frames.
• Emotion Recognition: The DeepFace library is used to analyze the emotions on the detected faces, providing a list of emotions and identifying the dominant one.
• Real-time Display: The dominant emotion is displayed on the video feed, and faces are highlighted with a rectangle.

• Python: Version 3.6 or higher
• Libraries:
  • OpenCV
  • DeepFace

• DeepFace for emotion analysis.
• OpenCV for face detection and video processing.