By completing this course, learners will be able to explain core computer vision concepts, apply edge detection techniques, build facial image datasets, train face recognition classifiers, and develop real-time face and eye recognition systems using OpenCV and Python.

This course provides a step-by-step, hands-on approach to face recognition, starting from foundational image processing concepts and progressing to a fully working real-time recognition system. Learners gain practical experience with edge detection algorithms such as Canny, learn how to collect and organize facial datasets, and understand how classifiers are trained and evaluated for recognition tasks. What makes this course unique is its project-driven structure, where every concept directly contributes to building a real application. Instead of isolated theory, learners see how preprocessing, detection, training, and recognition fit together in a complete pipeline. The course is ideal for beginners in computer vision as well as developers who want to implement, analyze, and deploy face recognition solutions using OpenCV. By the end of the course, learners will have the confidence and skills to build their own face recognition projects and extend them to real-world applications.