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Course Outline
Introduction to Artificial Intelligence and Image Processing
- What is Artificial Intelligence?
- Machine Learning vs. Deep Learning
- AI applications in law enforcement
Basics of Image Processing
- Digital images: pixels, resolution, and formats
- Image manipulation (brightness, contrast, resizing, cropping)
- Introduction to OpenCV for image processing
Understanding Neural Networks
- Basics of neural networks and how they work
- Introduction to Convolutional Neural Networks (CNNs) for image data
Facial Features Detection
- How AI models identify and differentiate facial features
- Using pre-trained models for face detection
Data Collection and Preparation
- Importance of quality datasets for training
- Data augmentation techniques to improve model performance
Training a Facial Recognition Model
- Overview of TensorFlow and Keras for deep learning
- Step-by-step guide to training a facial recognition model
Model Evaluation and Testing
- Metrics to evaluate facial recognition accuracy
- Techniques to improve model performance
Deployment of Facial Recognition Tools
- Building a simple application interface for end-users
- Integrating the model into law enforcement workflows
Ethical and Privacy Concerns
- Legal implications of using facial recognition in law enforcement
- Best practices to ensure ethical use
Advanced Tools and Future Trends
- Introduction to cloud-based facial recognition APIs (e.g., AWS Rekognition, Azure Face API)
- Exploring advanced neural network architectures for facial recognition
Summary and Next Steps
Requirements
- Basic computer literacy
Audience
- Law enforcement personnel
21 Hours
