This document describes an automatic attendance system using face recognition. It discusses the following: 1. The system was created by a team of 4 students to automate attendance taking and save faculty time. It uses OpenCV and face detection algorithms. 2. The system works by training on images of students taken from the class and stored in the database. It then detects faces in new images or video and identifies the students to mark attendance. 3. Key technologies used include OpenCV for image processing, Tkinter for the GUI, Pandas and NumPy for data handling, and algorithms like Haar Cascade and LBPH for face detection and recognition.

1. Automatic Attendance
System By Face
Recognition
Supervised by :-
Rajesh Pal Sir
Submitted By:-
Shitanshu Ranjan Srivastava [17-IEC-041]
Aniruddh Singh [17-ICS-008]
Ashwani kumar kushwaha [17/IEC/012]
Himanshu Kumar Singh [R16-IEC-015]

2. Table Of Content
01 – OUR TEAM
02 – INTRO TO OUR PROJECT
03 - WORKING MODULE
04 - DEMONSTRATION

3. OUR TEAM
Shitanshu
Ranjan
Srivastava [17-
IEC-041]
Ashwani Kumar
Kushwaha [17-
IEC-012]
Aniruddh
Singh [17-ICS-
008]
Himanshu
Kumar Singh
[R16-IEC-015]

4. BASICS WE SHOULD KNOW –
1. What do you mean by facial
recognition ?
2. Which model have we used for face
detectiom ?
3. Why has been OpenCV used ?
4. Which algorithm has been used ?
5. How to increase the accuracy of face
recognition ?
6. What is the role of tkinter in this face recognition ?

5. • The project is made to automate the student’s attendance
overhead by faculty and save time for lectures as well.
• It will also reduce the proxy of attendance to some extent. Not
sure to reduce to 100% until it’s implemented.
• The project is made in python using open CV (Open Source
Computer Vision Library).
• The software will take images of students in database in jpeg
format and this dataset will be trained and detected to identify
that particular student.
• The images can either be took from a class video or directly
through webcam as per convenience of faculty.
Introduction:-

6. This Photo by Unknown author is licensed under CC BY-NC-ND.
VS code
• Visual Studio
Code is a free
source-code
editor made
by Microsoft
for Windows
Open CV
• It is an image
and video
processing
library with
bindings in C,
C++, python
and java. It
will be used
for our facial
detection
Pandas
• Pandas is the
most popular
python library
that is used
for data
analysis. It
provides
highly
optimized
performance
with back-
end.
NumPy
• NumPy is a
python library
used for
working with
arrays. It also
has functions
for working
in domain of
linear algebra,
Fourier
transform,
and matrices
OpenpyXL
• Openpyxl is
a Python
library for
reading and
writing
Excel files.
The openpyxl
module
allows Python
program to
read and
modify Excel
files.
Tkinter
• : Tkinter is
the standard
GUI library
for Python.
Python when
combined
with Tkinter
provides a
fast and easy
way to create
GUI
applications.
Tkinter
provides a
powerful
object-
oriented
interface to
the Tk GUI
toolkit.
TOOLS and LIBRARIES USED:-

7. The Algorithim has four stages
Haar Feature
Selection
Creating
Integral Images
Adaboost
Training
Casading
Classifirers
Detection :- Detection is done by the help of
OpenCV and Haas cascades
This Photo by Unknown author is licensed under CC BY-SA.

8. Detection :
Detection is done by using HAAR CASCADE Algorithm.
The detection of face is done by clicking on attendance button on portal interface.
ON clicking the attendance button Web-cam or any integrated Camera gets open to
capture the images.
All the sample data of only face is taken and stored in the folder training images in the
root folder of project.
The haar -Cascade feature allows to only capture facial images and that is converted
into Grey scale by using fuction of opencv2 library imported in our project.
Gray= cv2.cvtColor(img, cv2.Color_BGR2GRAY)
To train faces We will be using haarcascade_frontalface_default.xml file

9. Haar Cascade
Working:

10. Recognition
OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can
use any of them by just changing a single line of code. Below are the names of those face
recognizers and their OpenCV calls.
1. Eigen Faces Face Recognizer - cv2.face.createEigenFaceRecognizer()
2. Fisher Faces Face Recognizer -cv2.face.createFisherFaceRecognizer()
3. Local Binary Patterns Histograms (LBPH) Face Recognizer -
cv2.face.createLBPHFaceRecognition
Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the
pixels of an image by thresholding the neighborhood of each pixel and considers the
result as a binary number.
In our project we will use Recognition by LBPH recognizer

13. Local binary patterns histograms (LBPH) Face Recognizer
Process
• We know that Eigenfaces and Fisher faces are both affected by
light and, in real life, we can't guarantee perfect light
conditions. LBPH face recognizer is an improvement to
overcome this drawback.
• Take a 3×3 window and move it across one image. At each
move (each local part of the picture), compare the pixel at the
center, with its surrounding pixels. Denote the neighbors with
intensity value less than or equal to the center pixel by 1 and
the rest by 0.
• After you read these 0/1 values under the 3×3 window in a
clockwise order, you will have a binary pattern like 11100011
that is local to a particular area of the picture. When you finish
doing this on the whole image, you will have a list of local
binary patterns.
• Now, after you get a list of local binary patterns, you convert
each one into a decimal number using binary to decimal
conversion (as shown in above image) and then you make a
histogram of all of those decimal values. A sample histogram
looks like this:

18. •ON clicking on attendance button the Camera window pops up.
•The window opens on the same screen and recognizes the face for marking attendance
•On pressing button ‘q’, the attendance gets marked.

20. Manage record in Excel files by GUI
• By the help of GUI CRUD operations can be performed in excel
files
Tkinter and openpyxl.
• With help of Tkinter we can make GUI which purpose is to insert
record in excel sheet.
• With the help of OpenPyxl we will access it.
• We will use pandas to convert xlsx to
• csv(Comma Separated Values) file for
• convenience to code and for machine to understand.

21. Train Image

22. • •We will use pandas to convert xlsx to csv(Comma Separated Values) file
for convenience to code and for machine to understand.
• •The student details are being entered automatically in excel sheet one
who registers them from GUI directly.

23. DEMONSTRATON

25. THANK YOU...