1. Introduction

A Face Recognition System is a biometric technology that identifies or verifies a person by analyzing facial features from an image or video frame. This project aims to develop an intelligent system capable of recognizing human faces in real-time or from stored data.

2. Objective

• The main goal is to design a system that:
• Detects a face in an image or live video.
• Extracts facial features.
• Compares them to a database of known faces.
• Identifies or verifies the person accurately.

3. Technology Stack

• Programming Language: Python
• Libraries Used:
• OpenCV for image processing and face detection.
• face_recognition library for facial encoding and matching.
• dlib for deep learning-based facial feature extraction.
• Optional: GUI using Tkinter or Flask for a web interface.

4. Working Process

1. Face Detection: Locate faces in input images or frames using Haar cascades or deep learning methods.
2. Feature Extraction: Convert each face into a numerical vector (face encoding).
3. Comparison: Match the encoding with those stored in the database using a similarity threshold.
4. Output: If a match is found, display the person’s name; otherwise, mark as “Unknown”.

5. Applications

• Security systems (e.g., door locks, surveillance)
• Attendance systems (e.g., schools, offices)
• Access control (e.g., airports, data centers)
• Personalized user experiences (e.g., smartphones, smart TVs)

6. Advantages

• Non-intrusive (no need for contact).
• Fast and user-friendly.
• Can work with video feeds in real-time.

7. Limitations

• Accuracy can be affected by lighting, angles, or occlusions (e.g., masks, sunglasses).
• Privacy concerns and data protection regulations need to be addressed.
• May require high computational power for large datasets or real-time use.

8. Conclusion

The Face Recognition System is a powerful tool for identity verification and surveillance. This project demonstrates the potential of combining computer vision and machine learning for practical, real-world applications.