The document presents a project on face recognition using a Raspberry Pi. It discusses using OpenCV for face detection with Haar cascades and eigenfaces for face recognition with PCA. The system consists of a Raspberry Pi connected to a camera. Faces detected in images are compared to a database of stored faces to recognize individuals and control access to a gate by opening or closing it. The project aims to develop a basic access control system using face recognition for security purposes.

1. A presentation on:
Face recognition with raspberry pi
GUJARAT TECHNOLOGICAL
UNIVERSITY
C. K. PITHAWALA COLLEGE OF ENGINEERING AND TECHNOLOGY

2. Prepared by:
Group no-5
Sr. no. Name of student Enrolment
1. Dimple Balasara
120090111055
2. Hetvi Naik 130090111055
3. Vatsal Champaneria 140093111005
4. Krunal Parmar
140093111017
5. Khushbu Raj 140093111033Guided by:
Dr. Mita Paunwala

3. Central Idea
A system for face detection and recognition based on raspberry pi with
open cv programming to control the gate.

4. Outlines
❑ Introduction
❑ Literature survey
❑ Block diagram
❑ Algorithm
▪ Face detection
▪ Database
▪ Face recognition
❑ Software
❑ Hardware
❑ The system
❑ conclusion

5. Introduction
► A facial recognition system is a computer application for
automatically identifying or a verifying a person from a digital
image.
► Face detection locate face in whole frame.
► Input image is matched with database images. Authentication is
given accordingly.
► Face recognition system is mainly used for security purpose.
► We are using raspberry pi for putting our plan into effect!

6. Contd…
Recognition is
addition of two parts:
1) Face Detection
2) Face recognition

7. Literature survey
Types Of
Biometrics
Explanation
Voice highly immune to
noise.
Fingerprint It needs high
observation and a
personal man-work.
Eyes Highly expensive.
Face Needs proper lighting
but much accurate
from above all
biometrics.
Face Detection
Methods
Explanation
Adaboost trainer Complexity
computational
LBHP algorithm Can’t judge sex of
person
Viloa jones (Haar
cascade)
Easy and gives
combination of
many.

8. Literature survey
Face recognition
methods
Explanation
Fisher face the reserve
information is not
useful
LBHP algorithm Can’t judge sex of
person
Eigen face(PCA) Reserves
information with
low dimension
Hardware Explanation
Micro-
controller/processor
Less memory and
delay
Embedded hardware Need to call the arms
and timing is delay
Raspberry pi It is credit-size
processor with
memory and GPIO
pins.

9. Block diagram
camera
[Face detection]
(Haar cascade)
Person
on
gate
i/
p
gate Match??
database
[Open cv + python code]
SD card memory
[Face recognition]
(Eigen faces)
yes/no
RASPBERRY
PI
motor
o/p
Yes
or
no
feedback
[Door opens/close]

10. Face Detection
Input from
camera
Feature
extraction
Feature vector
Face
detected?
Haar cascade
algorithm
Detected face
highlighted
NO
YES
Face database

11. OUTPUT

12. Database
► ORL Database
10 different images of each of 40 distinct subjects

13. Database
► Own Database
4 different images of each of 4 distinct subjects

14. Principal components analysis
►

15. Database
create training
set and load it
Convert face
images to face
vector
Calculate mean
average face
vector
Subtract average
face vector from
each face vectors to
have normalize face
vectors
Reduce
dimensionality
of training set
Calculate Eigen
vectors
Represent each
image as linear
combination of all
‘K’ Eigen vector
Select ‘K’ best
Eigen faces
PCA ALGORITHM

16. PCA [database]
►

17. ►

18. Eigen faces

19. Face recognition(even sem)
Input image
Convert it
into face
vector
Normalize
face vector
convert into
Eigen space
Get
weighted
vector
PCA ALGORITHM
Calculate “Distance” b/w
input weight vector and
all weight vectors of set
Decide a
threshold level
Decide
Distance >
threshold?
Display its
name and
“matched”
Display
Unknown
yes
no

20. PCA [recognition]
►

21. ►

22. Performance parameter

23. Threshold

24. Output of recognition
matched Not matched

25. software

26. Raspberry Pi B3
GERENIC DIAGRAM HARDWARE PHOTO
CPU/GPU USB HUBI/O
ETHERNET
RAM

27. On 1st boot of PI

28. The system
❑ Connections:
• Interface PI camera with
raspberry pi.
• Load database, programme and
raspbian OS in SD card and
inserted in to raspberry pi.
• Interface servo motor raspberry
pi through GPIO pins.
• Give power supply using battery
pack or mobile charger.

29. The outputs

30. conclusion
► By face recognition system we can fulfil the purpose of
security. Detection of face is done by image processing.
Here we use Open Cv with programming language C++.
► Initially camera will capture the image and face detection
algorithm will detect the face in image. Then recognition
algorithm is applied on this detected part. In this project we
have developed a PCA based face recognition system.

31. Reference
1. https://en.wikipedia.org/wiki/Biometrics
2. https://www.cse.unr.edu/~bebis/CS790Q/Lect/Chapters_3_4.ppt
3. http://eyalarubas.com/face-detection-and-recognition.html
4. https://www.raspberrypi.org
5. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
6. https://webdocs.cs.ualberta.ca/~nray1/CMPUT466_551/ViolaJones.ppt
7. M. Turk and A. Pentland “Eigen faces for Recognition”, Journal of Cognitive Neuroscience, vol.3, no.1, pp.71-86,
1991, hard copy
8. https://en.wikipedia.org/wiki/OpenCV
9. https://www.python.org/
10. www.numpy.org/
11. https://onionesquereality.wordpress.com/2009/02/11/face-recognition-using-eigenfaces
12. https://learn.adafruit.com/raspberry-pi-face-recognition-treasure-box/overview
13. https://www.youtube.com/channel/UCsRvxZErBo0ByyWUX_aVuvg (codacus)
14. http://www.imore.com/how-get-started-using-raspberry-pi
15. http://docs.opencv.org/doc/tutorials/introduction/windows_install/windows_install.html
16. https://www.youtube.com/watch?v=9hb0gYCv3YI
17. www.face-rec.org/databases/