Face Recognition with Keras: Detect & Classify

Overview

Learners will identify the principles of convolutional neural networks, analyze image data, apply preprocessing techniques, generate facial embeddings, and evaluate recognition models for real-world deployment. This hands-on course takes participants through the entire journey of building an advanced face recognition application with Keras. Starting with the foundations of CNNs and image preprocessing, learners will discover how to configure their systems, detect faces using MTCNN, and highlight features with bounding boxes and keypoints. The course then transitions into organizing datasets, generating embeddings with FaceNet, and constructing robust classifiers to recognize individual identities. By completing the course, learners gain practical experience in both face detection and recognition pipelines, bridging theory with implementation. They will acquire the ability to develop scalable computer vision applications, a highly sought-after skill in artificial intelligence and deep learning domains. What makes this course unique is its end-to-end, project-based approach: instead of focusing on isolated concepts, learners build a fully functional system, ensuring mastery of both foundational techniques and advanced deployment strategies.

Syllabus

Foundations of Face Detection & Computer Vision

This module introduces learners to the foundations of computer vision and face detection using Keras. It covers CNN principles, preprocessing techniques, model handling, and essential system setup, followed by practical implementation of face detection with bounding boxes and keypoints.

Building & Deploying Face Recognition Systems

This module focuses on transforming detected faces into numerical embeddings, building classification models, and deploying recognition systems in real-world scenarios. Learners progress from dataset handling to embedding generation, classifier training, and final implementation with Keras and FaceNet.