Real-Time Gender Classification and Hand Gesture Detection 🚀

An innovative project integrating gender prediction and face & hand gesture detection into a single, efficient real-time system, pushing the boundaries of human-computer interaction.

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🔍 Key Features

• Mini Xception Model:
  • Used for accurate and efficient gender classification.
• MediaPipe Framework:
  • Ensures precise face and hand gesture detection.
• Frame-Skipping Mechanism:
  • Maintains smooth and efficient real-time performance by optimizing frame processing.

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🛠️ Tech Stack

• OpenCV:
  • Handles video capture and real-time frame processing.
• TensorFlow:
  • Powers the Mini Xception model integration.
• NumPy:
  • Streamlines data manipulation and handling for efficient computation.

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📊 Performance Highlights

• Achieved 84% accuracy in gender classification and hand gesture detection.
• Demonstrated robust real-time processing capabilities across diverse environments.

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🌟 Applications

This project showcases the potential of AI and computer vision to bridge the gap between humans and machines, with applications in:

• Human-computer interaction
• Security systems
• Assistive technology
• Interactive gaming