Developed by: Farnaz Zinnah, Afia Nawar Jenice, Shashwata Kayum, Ahmed Nafis, Humaira Syed, and Juhi Rahman.

The Face Mask Detection Project employs advanced AI techniques to identify individuals wearing masks, aiming to assist in COVID-19 safety compliance. It integrates applications across web, Android, and iOS platforms using TensorFlow, Keras, Flutter, and Kotlin.

• Tech Stack: Python, TensorFlow, Keras, [Other Web Technologies]
• Features:
  • Real-time mask detection
  • User interaction and data visualization
  • Integration with cloud services for data storage
• Implementation Highlights:
  • Responsive UI
  • Live video processing
  • Effective cloud-based data management

• Built With: Kotlin, TensorFlow Lite
• Core Features:
  • Live camera integration for mask detection
  • Optimized performance for Android
  • Data synchronization with cloud backend
• Technical Aspects:
  • High accuracy detection algorithms
  • Minimal latency
  • Push notifications for mask compliance

• Framework: Swift, Flutter
• Capabilities:
  • Seamless iOS integration
  • Real-time mask detection with cloud support
  • Notifications system
• Innovations:
  • Custom AI models for iOS
  • High-performance design
  • User-friendly interface