This document describes an automated face recognition system for office door access control. It discusses introducing face recognition technology in the 1980s for security applications. The proposed system uses principal component analysis and artificial neural networks to recognize faces. It achieves 80% recognition accuracy between 40-60cm from the camera when the head is within 20 degrees left or right. The system involves training and evaluation stages, extracting features from faces during training and recognizing new faces during evaluation using the stored database. Graphical and neural network components are used to capture, crop, and recognize faces in real-time. Potential applications include security at ATMs, cameras, and public surveillance systems.

1. DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATION ENGINEERING
(2015-2016)
TAE- 3
Literature review PowerPoint Presentation
Application of Control System
Subject:
Control System
“Automated Face Recognition System for
Office Door Access Control Application ”
Year/Semester: 3rd Year | 6th Sem | Section: ‘C’
Submitted By:
Adarsh Pisey (24)
Akash J. Shahu(26)
Amol M. Wagh (27)
Ashish M. Pandey (30)
Submitted To:
Prof. Swapna Choudhari,
G.H.R.C.E., Nagpur.

2. INTRODUCTION
• The Security currently become a very important
issue in public or private institutions.
• A Various security System have been proposed
and developed for some crucial process:
Person Identification.
Person Verification.
Person Recognition.
Face Recognitions.

3. INTRODUCTION
• Face recognitions have been an active area of
research with numerous application since late
1980s and become one of the important
elements in security development system.
• An automatics Face recognition system with the
potential application for office door access
control technique is based on the (PCA) Principle
Component Analysis and Artificial neural network
have been applied into system.

4. DESCRIPTION
• In Detail Study of Face Recognition system purposely
built for office door access control includes the
analysis of influences of three main factors:
 Illumination.
 Distance.
 Head orientation.
• The System is been achieved good performance of face
recognition rate of 80% at the distance of camera and
subject between 40 cm to 60 cm and subject’s
orientation head angle must be within the range of
20% to +20 degree.

5. Block Diagram

6. Methodology and System
Development
• The development of this automated face
recognition system is done In typical face
recognition in two Stages.
1. Training Stages.
2. Evaluation Stages.

7. Training stages and Evaluation Stages
• In Training Stage, the specific number of training
images of face candidate is captured.
• The feature are extracted from the intensity
image of human frontal faces using principle
component analysis.
• The System will then learn on the extracted
feature and store them in its database.
• In the Evaluation Stage the system will recognize
new faces in an unsupervised manner and that is
easy to implement using artificial neural network.

8. Methodology and System
Development
• The Face recognisation is based on two type of
face database that is Graphical User Interface
and Artificial Neural Network.
• Graphical User Interface: It Captured and
Cropped Face image of person to be
recognize.
• Artificial Neural Network: It consist the ad-
hoc face frontal images that are captured
instantly using the system’s camera.

9. Application
• Security measure at ATM’s
• Digital Cameras
• Public Surveillance (CCTV’s) at Airports,
Hospitals, etc.
• Person Identification.
• Person Verification.
• Person Recognition.

10. Reference
• Ratnawati Ibrahim, Malaysia (IEEE Research
Paper).
• Zalhan Mohd Zin, Malaysia. (IEEE Research
Paper).
THANK YOU