Design And Implementation Of Face Recognition – Based Door Access Control System

With recent dramatic development in the field of artificial intelligence (AI), smart access control has become crucial part of our modern everyday lives. This paper presents a door security system designed to prevent trespassing in a highly secure areas like home environment. The implemented system is cheaper and more reliable for intruder detection and door security. The door access is solely based on face recognition to ensure that only authorized individuals go through the door. The principle on which face recognition works is that the sysadmin defines an individual or a group of individuals who are allowed to enter the area, a room or a building, through a door, a gate, or any physical barrier. Thus, the access is limited to these individuals.

Faces of authorized individuals are captured, stored, and trained by the system, and a real-time face is captured and matched against the ones of authorized individuals to identify the individual gaining access through the door after which the access is granted if that individual is authorized and denied otherwise. Haar-based cascade classifier was used for face detection while Local Binary Pattern Histograms (LBPH) algorithm was used for face recognition. 150 faces of individual were captured and trained after which real-time faces were tested on the system to determine the accuracy. The implemented recognition system achieved a recognition or accuracy rate of 88.6%. Notification has been achieved, using Simple Mail Transfer Protocol (SMTP), by sending emails to the sysadmin in case of any successful or unsuccessful attempt to gain access through the door. Python"s Open Source Computer Vision (OpenCV) library was used for face recognition. The implemented system is cheap, user-friendly, and reliable

Contents
Chapter 1: Introduction
1.2 Background
1.3 Problem Definition
1.4 Objectives
1.5 Scope

Chapter 2: Literature Review and Related Work
2.1 Literature Review
2.2 Related Works

Chapter 2: Design and Methodology
2.1. Design Decisions
2.1.1. Microcontroller
2.1.2 Programming Language

Chapter 3: Implementation
3.1 Face Recognition Module
3.1.1 Face Detection and Data Gathering
3.1.2 Face Recognition
3.1.2.1 Local Binary Pattern Histogram (LBPH) Algorithm
3.1.2.1.1 How it works
3.1.1 Face Detection and Data Gathering
3.2 Buzzer Module
3.3 Door Lock Module
3.4 Notification Module

Chapter 4: Results and Discussion
4.1 Face Recognition
Test 1
Test 2
Test 3
4.1.1 Challenges
4.1.2 Comparison to face recognition system

Chapter 5: Conclusion and Recommendations
Conclusion
Recommendation
References

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