How Facial Recognition Works with Machine Learning | IABAC

Explore how facial recognition leverages machine learning to identify and verify individuals. Learn the process of face detection, feature extraction, and pattern matching, along with applications, challenges, and the role of AI in enhancing accuracy and security.

1. How Facial Recognition
Works with Machine
Learning iabac.org‌

2. Introduction to Facial Recognition
Facial recognition is a technology that identifies or verifies a
person using their facial features.‌
It is widely used in security, authentication, and social media
applications.‌
Modern systems rely heavily on machine learning to improve‌
‌accuracy.‌
iabac.org‌

3. Face Detection: ‌Locate a face in an image or video.
Feature Extraction: ‌Identify unique facial landmarks (eyes, nose,‌
‌mouth, contours).‌
Face Representation: ‌Convert facial features into a numerical‌
‌format (feature vectors).‌
Matching & Recognition: ‌Compare feature vectors against a
database for identification or verification.‌
Key Steps in Facial Recognition
iabac.org‌

4. Role of Machine Learning
Machine learning models, particularly deep learning and convolutional
neural networks (CNNs), analyze facial patterns.‌
The system learns from large datasets of labeled faces to recognize
variations in lighting, angles, and expressions.‌
Continuous training improves accuracy and reduces false matches.‌
iabac.org‌

5. Applications & Challenges
Security and surveillance‌
Smartphone unlock and identity verification‌
Attendance tracking and personalized experiences‌
Applications
Challenges
Privacy concerns and data security‌
Bias in datasets leading to accuracy disparities‌
Environmental factors affecting recognition
(lighting, occlusion)‌
iabac.org‌

6. Thank you
Visit: www.iabac.org
iabac.org‌