I'll create a real-time face recognition app that keeps the camera open continuously and sends frames to the backend for recognition.

I've created a real-time face recognition app with the following features:

Key Features:

• Continuous Camera Feed: Square format camera that stays open
• Real-time Frame Processing: Sends frames to backend every 2 seconds
• Recognition Status: Shows green for recognized, red for not recognized
• Live Feedback: Displays recognition messages and confidence scores
• System Status: Shows camera and recognition service status
• Dark Theme: Professional security system appearance

How It Works:

1. Camera Initialization: Automatically starts camera on page load
2. Frame Capture: Captures frames every 2 seconds from the video feed
3. Backend Communication: Sends base64 image data to /api/recognize endpoint
4. Response Processing: Receives recognition flag and displays appropriate message
5. Visual Feedback: Changes background color and icon based on recognition status

Backend API:

• Endpoint: /api/recognize (POST)
• Input: Base64 image data
• Output: Recognition flag, confidence, name, and message
• Mock Logic: Currently returns random results for demonstration

Recognition States:

• Green: Face recognized successfully
• Red: Face not recognized
• Gray: System initializing or no recognition attempt
• Processing: Yellow indicator when analyzing frame

To integrate with a real face recognition service, replace the mock logic in the API route with calls to services like AWS Rekognition, Azure Face API, or your custom ML model.