This is age of Modern Technology and it's becoming necessity for everyone. Our project is on one of the most basic daily life security system. As there was a time, when you had to open the door by yourself or u needed a key of some sort or a person for guarding some room. our project changes that view, as we have automated that old method. It's user friendly and no human interaction is needed.Door unlocking to provide essential security to our homes, bank lockers , server rooms , private chambers and offices etc.

1. Group Members :
Arslan Haider
Zohaib Arshad
Hassan Tariq
Aamir Mehboob
For Door Unlocking
Raspberry Pi Based Face
Recognition System
Project Supervisor :
Engr. Rizwan Qureshi

2.  Why this project?
• Being a student of engineering, we have had experience in programming but
never got a chance to develop on hardware such as the Raspberry Pi. The idea
of working with real world hardware, and knowing that this technology is used
worldwide in Blu-ray disks as well as by website like YouTube, Vimeo and iTunes
Store, motivated me further to take up this project.
 Objectives of this project :
• Research into video processing
• Learn more about video streams
• Get familiar with Raspberry Pi
• Interfacing Raspberry Pi with Hardware.
Project Objectives

3. • A facial recognition system is a computer application capable of identifying or
verifying a person from a digital image or video frame.
• It is typically used in security systems and can be compared to other
biometrics such as fingerprint or eye iris recognition systems. Recently, it has
also become popular as a commercial identification and marketing tool.
Advantage of using face recognition:
 Least intrusive
 More Secure.
What is Face Recognition System ?

4. • There are many identification systems but face recognition is now-a-
days more preferred.
• No Physical Interaction.
• It’s not that expensive to install/implement.
• Like Security purposes (As of Door unlocking), Attendance system,
face lock for mobile devices.
• Snapchat, Geo tagging(Facebook Auto Tags), law enforcement
agencies etc.
Why Face Recognition is Needed ?

5. • An important difference with other biometric solutions is that faces
can be capture from some distance away, with for example
surveillance cameras. Therefore face recognition can be applied
without the subject knowing that he is being observed (Security
Purpose).
Important Plus Point

6. • Automatic Identification & Verification
• Database of faces
• Fisher Faces
• Comparison
• Face Match
• Applications
Introduction

7.  For face detection, OpenCV cascade classifiers will be used.
 These trained classifiers include detectors of face, eyes, nose and
whole body, etc.
Face Detection

8. • The Viola–Jones object detection framework is the first object
detection framework to provide competitive object detection rates in
real-time.
Haar Cascade Classifier

9. • Eigen Faces
• Fisher Faces
• Local Binary Pattern
Three Popular Algorithms For Recognition

10. • Developed in 1997 by P.Belhumeur et al.
• Based on Fisher’s Linear Discriminant Analysis (LDA)
• Faster than eigenfaces, in some cases
• Has lower error rates
• Works well even if different illumination
• Works well even if different facial express.
Fisher Faces

11. • LDA maximizes the between-
class scatter.
• LDA minimizes the within-
class scatter.
• LDA seeks directions that are
efficient for discrimination
between the data.
Class A
Class B

12. • Raspberry Pi Board
• Power Supply
• Relay
• Power Adapter
• Camera Module
Components for Production of Project

13. • Interfacing of camera module to capture live face image.
• Create a database of authorized person.
• Capture current face, save it and compare with database.
• Interface relay as output module.
Proposed Work

14. Divided into 3 Parts:
• Camera Module
• Raspberry Pi Module
• Electronic bolt Lock
Working and Methodology
optional

15. Flow chart

16. 1st Phase

17. • Take Frame
• Detect Face
• Extract the Face
• Resize
• Save Extracted Face
Saving the Face Portion
Take
Frame
Detect
Face
Extract
the Face
Resize
Save
Extracted
Frame

18. Real Time Training
Input
Person
Name
Take
Frame for
14 Sec
Detect
Face
Save Face
Loop
terminated

19. • Finally the Faces of persons will be saved in prove folder with the
names of the persons.
• Those faces will then be used to recognize face.
Cont.

20. 2nd Phase

21. • Get Faces from Training
Folder
• Compute Model
• Get Real-time Frame
• Detect Face
• Match that face
• Predict Name
Real Time Face Recognition

22. Face Recognition Algorithm
Model Model.Train Model.Predict
Label index
&
Threshold
If Threshold
<= 800
Names[index]

23. Circuit Diagram

24. • A Raspberry Pi is a general-purpose computer, usually with a Linux operating system, and the
ability to run multiple programs.
• Very Low Cost($25-Rs 1550) for Model A & ($35-Rs 2200) for Model B/B+
• Lighter, Smaller, Efficient.
• Lower power consumption (less than 5W).
• Supports Full HD video (1080p),Multiple USB Ports etc.
• Smartcard swapping, alcohol detection and agriculture humidity sensing etc.
Why Raspberry Pi ?

25. Smart Surveillance Monitoring Security
 Living body detection and Spying
Attendance System
Criminal Recognition and Identification system
Future Scope

26.  The developed scheme is cheap ,fast, highly reliable and provides
enough flexibility to suit the requirements of different systems.
Extra Features:
 Counter: Counting the persons inside the specific room.
 Attendance system: Attendance and Displaying there names.
Features

27. THANK YOU