Face Alignment and Preprocessing with MediaPipe and Python

Raw facial images often suffer from tilt, poor lighting, and varied angles, making them difficult for machine learning models to analyze. Learning how to standardize and align these images is a fundamental preprocessing step for any serious computer vision pipeline. This text-based course guides you through the process of detecting facial landmarks, locating key features like the eyes, and mathematically rotating faces to a standardized, upright position using Python and MediaPipe. You will build a solid foundation in facial geometry, coordinate systems, and image transformation. What you'll learn: - Understand the core concepts of facial landmarks and geometric alignment. - Detect facial keypoints accurately using the MediaPipe framework. - Calculate rotation angles using eye coordinates and mathematical formulas. - Apply affine transformations to rotate and scale facial images cleanly. - Write clean, modern Python code utilizing type hints for robust image processing pipelines. - Structure preprocessed image datasets ready for downstream machine learning tasks. The course begins with foundational concepts of digital images and coordinate systems, transitions into hands-on landmark detection, and concludes with practical alignment and normalization techniques. You will learn through clear written explanations and step-by-step code snippets. This course is designed for beginners in computer vision, Python developers, and aspiring data scientists. No prior experience with image processing or advanced mathematics is required. Start reading today to build cleaner, more accurate facial analysis pipelines.