Build practical face recognition skills with Keras, convolutional neural networks (CNNs), MTCNN, and FaceNet. Designed for learners who want hands-on experience in computer vision and deep learning, this project-based course guides you through building a complete face detection and recognition system.

You’ll begin with CNN principles, image preprocessing, model management, and deep learning environment setup. You’ll then use MTCNN to detect and localize faces, visualize bounding boxes and keypoints, and analyze results across multiple images. As you progress, you’ll organize face image datasets, generate numerical facial embeddings with FaceNet, and construct supervised classifiers that distinguish individual identities. You’ll also evaluate recognition performance through real-world testing and integrate FaceNet with Keras for deployment. What makes this course distinctive is its end-to-end approach: rather than studying detection and recognition as isolated concepts, you’ll connect preprocessing, face detection, dataset preparation, embedding generation, classification, evaluation, and implementation in one practical workflow. Enroll to gain the skills to build and assess functional, scalable face recognition applications for real-world scenarios.