Overview
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AI-based face recognition is a biometric technology that uses AI, deep learning, and convolutional neural networks (CNNs) to analyze and identify individuals by scanning unique facial features—such as eye spacing, nose shape, and jawline—to create a digital "faceprint" or map. It enables fast, contactless authentication for applications like phone unlocking, security surveillance, attendance systems, and personalized marketing.
How AI Face Recognition Works
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Face Detection: Algorithms isolate faces from backgrounds in images or live video, often distinguishing multiple faces in crowded settings.
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Feature Extraction: The system maps key facial landmarks (eyes, nose, mouth) to create a unique digital signature or embedding.
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Encoding & Matching: The software compares the generated signature against a database of known faces to find a match.
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Liveness Detection: Modern systems often detect if the face is a real person rather than a photo, improving security.
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Our Trainers
Aman Sharma
Aman Sharma is an AI & ML Instructor at Saksham Data Analyst & Tech Digi Pvt Ltd.
He specializes in teaching AI concepts with clear, practical examples.
Aman is passionate about helping learners build real-world AI projects.
He enjoys making complex AI topics easy and engaging for everyone.
Modules
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Day 1: Introduction to AI & Face Recognition What are AI & computer vision? Real-world applications Course overview.
Day 2: Python Basics Revision Variables, loops, functions Installing Python & libraries.
Day 3: Introduction to OpenCV image reading & display camera access.
Day 4: Face Detection Using Haar Cascade What is Haar Cascade face detection in an image?
Day 5: Real-Time Face Detection Webcam face detection: Draw a bounding box.
Day 6: Image Processing Basics Grayscale conversion, resize, blur.
Day 7: Mini Project Build: Face Detection System.
Day 8: Introduction to face recognition detection vs. the face encoding recognition concept.
Day 9: face_recognition Library Install & Setup Encode faces.
Day 10: Face Matching System • Compare known vs unknown faces • Confidence score
Day 11: Multiple Face Recognition Recognize multiple people Label faces.
Day 12: Attendance System Logic Store data (CSV/Database) and mark attendance.
Day 13: Final Project Development Build: Face Recognition Attendance System.
Day 14: Project Presentation, Demo Project, Interview Questions.
Outcomes
After 2 weeks, students will:
• Understand AI & computer vision basics
• Build a real-time face detection system
• Implement face recognition
• Create a real-world project (Attendance System)
• Gain internship-ready skills
