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AI-powered facial recognition
Skill Developement

AI-powered facial recognition

Face recognition is an application of artificial intelligence and computer vision that identifies or verifies a person using their face from images or video.
Course ModeOnline
Duration2 Weeks
Starts29 Jun 2026
Ends11 Jul 2026
Course Fee
₹1,000 /-
Registration Closed
About The Course

Overview

  • 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

    1. Face Detection: Algorithms isolate faces from backgrounds in images or live video, often distinguishing multiple faces in crowded settings.

    2. Feature Extraction: The system maps key facial landmarks (eyes, nose, mouth) to create a unique digital signature or embedding.

    3. Encoding & Matching: The software compares the generated signature against a database of known faces to find a match.

    4. Liveness Detection: Modern systems often detect if the face is a real person rather than a photo, improving security.

Learning Goals

Course 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

Curriculum

Course / Modules

  • 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.

     

Learn From Experts

Our Trainers

Aman Sharma
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, practica...