1. Presentation on
Automatic
Attendance System
with Real-Time Face
Recognition

2. Team
members
Name: Rudra Prasad Sabar
Name: Ehsanulllah Noori
Name: Biswajit Sarangi Name: Subhakanta Behera
Mentor: Hara Prasad Naik

3. Topic Cover
Introduction
What are biometrics?
What is face recognition?
Why we choose face recognition?
System analysis and how the system works

4. Implementation of face recognition technology
How face recognition system works
face recognition attendance system
Merit, Demerit and its application
Conclusion and Future Scope

5. Traditionally, student’s attendances are taken manually
by using attendance sheet given by the faculty in the
class, which is a time consuming event.
FACE RECOGNITION technology is gradually evolving
to a universal biometric solution since it requires
virtually zero effort from the user end while compared
with other biometric options. It is accurate and allows
for high enrolment and verification rates.
Moreover, it is very difficult to verify one by one
student in a large classroom environment with
distributed branches whether the authenticated
students are actually responding or not.

6. what are
Biometrics ?
A biometric is a unique, measurable characteristics of a human being that
can be used to automatically recognize an individual or verify an individual’s
identity. Biometrics can measure both physiological and behavioural
characteristics. Physiological biometrics (based on measurements and data
derived from direct measurement of a part of the human body) include:
Types of Biometrics
Physiological Behavioral
o Face recognition
o Finger-scan
o Iris-scan
o Hand-scan
o Ratina-scan
o Voice-scan
o Signature-scan
o Keystroke-scan

7. Face recognition is a method of identifying or
verifying the identity of an individual using their
face. Face recognition systems can be used to
identify people in photos, video, or in real-time.
(Or) A facial recognition is a computer
application for automatically identifying or
verifying a person from a digital image or a video
frame from a video source.
❝
❞

8. Why we choose face recognition?
Reasons
It can use your existing hardware
infrastructure, existing cameras and
image capture.
Overall it is the most efficient system
to collect automatic student
attendance of online class in corona
pandemic.
It is the only biometric that allow you
to perform passive identification in a
one to many environments
It does not require an expert to interpret
the comparison result.
It is accurate and allows for high
enrolment and verification rates.
It requires no physical interaction on
behalf of the user.

9. System Analysis
System analysis is conducted for the purpose of studying a system to
identify its objectives. It is a problem solving technique that improves
the system and ensures that all the components of the system work
efficiently to accomplish their purpose.
Analysis specifies what the system should do.
How the system
works
There must be an active camera that captures the picture of students.
The system controller should get a picture of the student and save it in the folder
inside the project.
The captured image must be detected and recognized by the system, the system
shows the name of the student when the student comes in front of the camera.

10. The system must save its name, time, and date in an Excel sheet which
is stored inside the project.
The system must not recognize the faces which are not stored inside
the system.
The system must store the name of a student multiple time, it must recognize it
continuously but stores only once.
They system must announce the name of the student while capturing
and recognizing their faces like: “Welcome to class Rudra”.

11. Project Requirement
Hardware
Software
pc with webcam, h/w disk-1TB
OS-windows 10 64bit
Progamming language- Python, PycharmCommunity(IDE)
Database- Excel

12. Image
acquisition
Image
processing
Extraction of facial
features
Comparing with
database
Marking the attendance
Block
diagram

13. Facial-scan technology can acquire faces from
almost any static camera or video system that
generates images of sufficient quality and
resolution.
High-quality enrolment is essential to eventual
verification and identification enrolment images
define the facial characteristics to be used in all
future authentication events.
Image
acquisition

14. Images are cropped and colour images are
normally converted to black and white in order
to facilitate initial comparisons based on grey
scale characteristics.
First the presence of faces on faces or face in a
scene must be detected. Once the face is
detected, it must be localized and normalization
process may be required to bring the
dimensions of the live facial sample in
alignment with one on the template.
Image
processing

15. All facial-scan systems attempt to match
visible facial features in a fashion similar to the
way people recognise one another.
The features most often utilized in facial-scan
systems are those least likely to change
significantly over time: upper ridges of the eye
sockets, areas around the check bones, sides of
the mouth, nose shape, and the position of
major features relative to each other.
Extraction of facial
features

16. Every face has at least 80 distinguishable parts called nodal points.
Or, there are about “80 nodal points” on a human face.
Here are few nodal points that are measured by the software.
 distance between the eyes
 width of the nose
 depth of the eye socket

 structure of the cheekbones
 length of jawline
 chin

18. 1 import cv2
2 import numpy as np
3 import face_recognition as face_rec
4 import os
5 import pyttsx3 as textSpeach
6 from datetime import datetime
Importing libraries

19. 7 def resize(img, size) :
8 width = int(img.shape[1]*size)
9 height = int(img.shape[0] * size)
10 dimension = (width, height)
11 return cv2.resize(img, dimension, interpolation= cv2.INTER_AREA)
Resizing the window

20. 12 path = 'Students'
13 studentImg = []
14 studentName = []
15 myList = os.listdir(path)
16 print(myList)
17 for cl in myList :
18 curimg = cv2.imread(f'{path}/{cl}')
19 studentImg.append(curimg)
20 studentName.append(os.path.splitext(cl)[0])
Taking the photos from the folder

21. 20 def findEncoding(images) :
21 imgEncodings = []
22 for img in images :
23 img = resize(img, 0.50)
24 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
25 encodeimg = face_rec.face_encodings(img)[0]
26 imgEncodings.append(encodeimg)
27 return imgEncodings
28 print(imgEncodings)
Encoding the images

22. 29 def findEncoding(images) :
30 imgEncodings = []
31 for img in images :
32 img = resize(img, 0.50)
33 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
34 encodeimg = face_rec.face_encodings(img)[0]
35 imgEncodings.append(encodeimg)
36 return imgEncodings
37 print(imgEncodings)
Finding encoding

23. 38 vid = cv2.VideoCapture(1)
39 while true:
40 success, frame = vid.read()
41 Smaller_frames = cv2.resize(frame, (0,0), None, 0.25, 0.25)
42 facesInFrame = face_rec.face_locations(Smaller_frames)
43 encodeFacesInFrame = face_rec.face_encodings(Smaller_frames, facesInFrame)
Capturing the video from Camera

24. Comparing the encoded faces
44 for encodeFace, faceloc in zip(encodeFacesInFrame,
facesInFrame) :
45 matches = face_rec.compare_faces(EncodeList, encodeFace)
46 facedis = face_rec.face_distance(EncodeList, encodeFace)
47 print(facedis)
48 matchIndex = np.argmin(facedis)
49 if matches[matchIndex] :
50 name = studentName[matchIndex].lower()
51 y1, x2, y2, x1 = faceloc
52 y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
53 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)
54 cv2.rectangle(frame, (x1, y2-25), (x2, y2), (0, 255, 0),
cv2.FILLED)
55 cv2.putText(frame, name, (x1+6, y2-6),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 56 255, 255), 2)
57 MarkAttendence(name)
58 cv2.imshow('video',frame)
59 cv2.waitKey(1)

25. connecting with attendance
60 def MarkAttendence(name):
61 with open('Attendace.csv', 'r+') as f:
62 myDatalist = f.readlines()
63 nameList = []
64 for line in myDatalist:
65 entry = line.split(',')
66
67 nameList.append(entry[0])
68 if name not in nameList:
69 now = datetime.now()
70 timestr = now.strftime('%H:%M')
71 f.writelines(f'n{name}, {timestr}')
72 statment = str('welcome to class' + name)
73 engine.say(statment)
74 engine.runAndWait()

26. Test
Images

27. face recognition attendance system
The modern face recognition attendance system is much different
and safer than the conventional counterparts. There are a few
attendance systems that are based on the face recognition
technology and each of them has extremely varying features for the
best suitability in different environments.
It provides a facility for the central data management system.
A very cost effective method of recording the attendance.

28. Attendance
in
excel
File

29. Attendance
File

30. Advantages
Automated time tracking
system
Cost effective
Easy to manage
Increased security

31. Applications
Primary application being used in the classrooms to take the attendance of the
students.
Security: Access control, comparing surveillance images to know
terrorist.
ATM: The software is able to quickly verify a customer’s face.
Healthcare: Minimize fraud by verifying identity.
Decrease the false attendance.

32. CONCLUSION
Before the development of this project. There are many
loopholes in the previous method while taking attendance using old method which
caused many troubles to most of the institutions.
Therefore, the facial recognition feature method is secure enough, reliable and
available for use.
It saves time and lot of effort,especially if it is a lecture with huge number of
students.

33. Some of the future enhancements that can be done to this
system are as follows:
Facility of Defaulter list in which system update the
defaulter student.
For students, we can develop a dedicated login page in
which they can see their details and attendance record.
In future we can update the modules like notices for student
and many more.
Future Scope