This document outlines a research project proposal for implementing real-time face recognition on an attendance system. The project aims to use machine learning and computer vision techniques to detect student faces and recognize their names for attendance tracking. The proposal discusses conducting an initial prototype using Python, OpenCV, NumPy and local binary pattern (LBP) classification. It describes collecting a database of facial images, developing the system design using use case, activity and sequence diagrams. The work plan outlines developing the prototype over several months. The goal is to gain experience with computer vision tools and apply face recognition to applications like security, banking and more.

1. “REAL TIME FACE RECOGNITION IMPLEMENTATION
ON ATTENDANCE SYSTEM”
(RESEARCH BASED MACHINE LEARNING PROJECT)
Debendra Adhikari
Supervisor: Manoj Shrestha
Committee Members: Som Prasad Shrestha
Prakash Ranabhat
Pradeep Acharya
IT PROJECT I : PROPOSAL DEFENSE
Department of Computer Science (BCS)
Infrastructure University Kuala Lumpur
August 22,2018

2. Overview
 Introduction to Face Recognition System
 Literature Review
 Statement of Problem
 Functional and Non Functional Requirements
 Objective
 Method of Development/Methodology- Design
 Work Plan
 Conclusion
 Simulation View
 References

3. Introduction
 Face detection is the base for face tracking and face
recognition.
Related to the Biometrics and Computer Vision
Programming.
One of the challenging task in pattern recognition
research.
In face recognition there are 2 types of
comparisons:

4. Continued…
Verification and Identification.
Four steps involved during identification and
authentication:
 Capture
 Extraction
 Comparison
 Checking Match

5. Facial Recognition…?
It requires no physical interaction on behalf of the user.
It is accurate and allows for high enrolment and
verification rates.
It can use existing hardware infrastructure, existing
cameras.

6. How Facial Recognition Systems
work.??
The Software/Model measures:
Distance between the eyes
Width of the nose
Depth of eye sockets
The shape of the cheekbones
The length of jaw line
These creates a numerical code, called a face print
representing the face in database.

7. Literature Review
In the 1960s, scientists began work on using the computer to recognize
human faces.
 Since then, facial recognition software has come a long way.
 Facial recognition software is based on the ability to recognize a face and
then measure the various features of the face.
 Every face has numerous, distinguishable landmarks, the different peaks
and valleys .
 The proposed system is based on the Python using various modules and
library like NumPy ,OpenCV during the initial prototype test and using
CNN(Deep Learning).

8. Literature Review : History
 In 1960s, the first semi-automated system for facial
recognition to locate the features(such as eyes, ears, nose and
mouth) on the photographs.
 In 1970s, Goldstein and Harmon used 21 specific subjective
markers such as hair color and lip thickness to automate the
recognition.
 In 1988, Kirby and Sirovich used standard linear algebra
technique, to the face recognition.

9. Literature Review : Face Recognition
Features
Face RecognitionFace Recognition
Global FeaturesLocal Features
LDAGaborLBP PCA

10. Literature Review : Face Recognition
Features
Global Features:
 Focus on whole Entire Image
 Less Accuracy
Local Features:
 Focus on the local features of the face, which help to
identify and verify the person
 More Accuracy

11. Proposed Solution : LBP Analysis
LBP Analysis :
Getting each
pixel of an
image as a
block in matrix
Result in
Decimal
number format
Result in
Binary number
format
Threshold each
matrix pixels
with the center
pixel of the
image
Figure : LPB Threshold

12. Working Process
Input Image
Face
Detection
Database Face
Recognition
Match/not-
match
Compare
Figure :Working Process of the Proposed Model

13. Statement of Problem
Face recognition is not an easy task for computers as we human .
The accompanying problem have existed for computer vision and
the project is based on research to solve the problems:
 Pose variation (outward appearance and facial position)
 Illumination conditions
 Scale changeability
 Images taken years apart
 Glasses, Moustaches, Beards
 Low quality image acquisition
 Partially occluded faces.
.

14. Continued…
 Invariant Pose of Face induce very large changes in face
appearance.
 Recognition rates fall drastically when images from two
different poses of same person are matched.
 Almost all the face databases have frontal faces. So for non-
frontal faces features we require many train sets of data.
 Here our proposed system is initially based on LBP Classifier for
prototype which is faster and more accurate.

15. Continued…
 LBP is a visual/texture descriptor, and our faces are also
composed of micro visual patterns.
 LBP features are extracted to form a feature vector that classifies
a face from a non-face.

16. Functional Requirements
 User (Lecturer) should be able to create their own account
with their respective course name, course code and
designation.
 User (Lecturer) should be able to set the calendar date of
the particular day and open the webcam.
 User (Administration) should be able to download/extract
the excel sheet of the present student.

17. Non -Functional
Requirements
 Usability
 Accuracy
 Reliability
 Supportability

18. Objective
The main objectives of this project are enlisted below:
 To gain deeper understanding of OpenCV, Python, NumPy and
Machine Learning Models.
 To gain insights of Supervised and Unsupervised Learning.
 To create a model to detect the face of Student and recognize
them with their name.
 To learn the various tools and modern technologies of Computer
Vision Application.

19. Continued…
 To learn the time management skills necessary to perform during
the Project days that will help us to gain the necessary
organizational skills.
 To establish a platform for other to create and work on machine
learning projects or share the related knowledge's.

20. Methodology
For the project ,Prototyping Model will be used based on Testing
the initial model using Python and OpenCV.
Figure : Prototyping Model

21. System Model
Use case Model
Identifying Actors
Proposed Face Recognition Implementation on Attendance
system consists of the following Actors.
User (Lecturer)
The end user that will use the system for taking attendance.
System Admin
Administrator of the overall system who can train the model with training
images and perform the necessary maintenance and operation.

22. Figure: Use Case Diagram

23. Activity Diagram
Figure : Activity Diagram

24. Sequence Diagram
Figure : Sequence Diagram I

25. Sequence Diagram
Figure : Sequence Diagram II

26. Class Diagram
Figure : Class Diagram

27. Work Plan
Project plan Using Gantt Chart.

28. Work Plan

29. Revised Project Plan .

30. Required Tools and Technologies
Here, is a list of minimum hardware requirements:
 Intel Pentium Processor, or other of 1GHz or greater
(64bit system configuration recommended).
 Minimum 128MB of RAM capacity or more.
 Minimum 32MB Graphic Card RAM capacities or more.
 Recommended Hard disk space of 500GB or more.

31. Continue…
Here is a list of software requirements:
 Windows 7 or above, Linux, Mac OS X.
 Python 2.5 or Python 3.3
 Jupyter Notebook
 OpenCV 3

32. Application
 Security/Counterterrorism. Access control, comparing
surveillance images to know terrorist.
 Day Care: Verify identity of individuals picking up the
children.
 Residential Security: Alert homeowners of approaching
personnel.
 Voter verification: Where eligible citizens are required to
verify their identity during a voting process.
 Banking using ATM: The software is able to quickly verify a
customer’s face.

33. Current Status
 Completed all Analysis and Requirement
Collection.
 Completed the Design and Simulation for
Initial Prototype.
 Collecting Data and around 5GB of facial data
has been collected from various sources.
 Working on creating the database for training
images.

34. Simulation
Let see the first initial simulation………….

35. Future Plan
 Smart Email Notifications to the Parents.

36. Conclusion
 LBP(Local Binary Pattern) is used to extract
the local features in the face and match it
with the most similar face in the image
database.
 Based on Research on Various Classifier of
OpenCV and Implement the best one on the
attendance system for maximum accuracy.

37. References
 S. Kherchaoui and A. Houacine, 2010, “Face Detection Based On A
Model Of The Skin Color With Constraints And Template Matching”,
Proc. 2010 International Conference on Machine and Web
Intelligence, pp. 469 - 472, Algiers, Algeria.
 http://ecomputernotes.com/software-engineering/explain-
prototyping-model Prototyping model in Software Engineering.
 History- www.biometrics.gov
 Brian C. Becker, Enrique G.Ortiz, “Evaluation of Face
Recognition Techniques for Application to Facebook ” IEEE,
2008.
 Solem, J. E. (2012). “Programming Computer Vision”.

38. Any
Questi
ons
Any
Questions….?