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

Delivery Options

Private Group Training

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  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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