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SECURITY
USING
FACE RECOGNITION
INTRODUCTION
 Face recognition is a process of scanning a face and
matching it against a library of known faces.
 Facial features of an image are extracted and a template is
generated.
 While matching, the features of the input image is
extracted and a template is generated.
 The template is compared against all the templates in the
database.
 If the match is found then the user is acknowledged as a
valid user and the alarm circuit is deactivated.
 In enhancement of the project ie attendance management
system for employees, once the user is validated as an
authorised user he will be marked as present else absent.
 We are also generating an attendance report.
Problem Statement
 To build an efficient security system which uses face
recognition technique.
 Authenticating users based on their facial images
which are compared against those present in the
database.
 An alarm circuit that will be activated as soon as an
intruder tries to break through the system.
Modules in face recognition
DESCRIPTION
 Sensor Module: It captures face images of individuals.
 Face Detection and feature extraction module:
Human faces are detected and their features are extracted
to form the template.
 Identification Module: The template extracted from
the input image is compared against stored templates in
the database.
 System Database Module: This module is responsible
for enrolling users in a face recognition system database.
EXTRACTION OF AN IMAGE
Fig 2.a Feature extraction of an Image
MATCHING
Fig 2.b Matching process
STEPS IN PCA ALGORITHM
 Organize the data set and calculate empirical mean:
Data is organized into a set of matrices and empirical
mean(U) is calculated.
 Calculate the deviations from the mean: A new matrix
B stores the mean subtraction. This is used to minimize the
mean square error.
 Find the covariance matrix : Covariance matrix C is
computed by multiplying matrix B by its transpose.
 Find the eigenvectors and eigen values of the
covariance matrix: The diagonal matrix D is
calculated of the covariance matrix C.
 Rearrange the eigenvectors and eigen values : The
eigen vector matrix V and eigen value matrix D is
sorted in the order of decreasing eigen weights.
 Select a subset of the eigenvectors as basis vectors:
Save the first L columns of V as the M  L matrix W:
 Convert the source data to z-scores (optional):
Create an M  1 standard deviation vector s from the
square root of each element along the main diagonal
of the diagonalized covariance matrix C.
 Project the z-scores of the data onto the new basis:
Electronic Circuit Diagram
WORKING
 IC mct2e receives the output from the computer via
its First and Second pins.
 If the computer sends a high output i.e. +5Volts when
the face does not match the LED inside the IC glows.
 This illumination of the LED is detected by the
transistor which inturn sends the signal to the relay
and turns it into an ON state.
 When low signal is sent by the computer then the low
signal(0 Voltage) is sent by the computer to the IC
mct2e and the Relay is switched off.
ALARM CIRCUIT
WORKING
 When the Relay is on the current flows through the
timer and the alarm circuit is active.
 If anyone touches the touch plate the buzzer rings and
the intruder is detected.
 When the Relay is off the 555 timer is disconnected
from the power supply and the circuit is deactivated.
CONCLUSION
 Using Principle component analysis the face
recognition was implemented using c# with the help
of small set of images.
 The system performance depends on the images
stored in the database and the input image provided
by the user.
 This system identifies the users based on any facial
images.
 The monthly attendance report for the employees was
successfully generated.
FUTURE SCOPE
 Techniques like 3-D modelling, neural networks can be
used.
 Different circuits like door circuit, vibrator sensor circuit
can be integrated with face recognition to fulfill different
security objectives.
REFERENCES
 http://encyclopedia.jrank.org/articles/pages/6741/Face
-Recognition.html
 http://www.electronicsforyou.com
 http://www.howstuffworks.com
 http://www.nec.com/global/solutions/security/technol
ogies/face-recognition.html
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SECURITY

  • 2. INTRODUCTION Face recognition is a process of scanning a face and matching it against a library of known faces. Facial features of an image are extracted and a template is generated. While matching, the features of the input image is extracted and a template is generated. The template is compared against all the templates in the database.
  • 3. If the match is found then the user is acknowledged as a valid user and the alarm circuit is deactivated. In enhancement of the project ie attendance management system for employees, once the user is validated as an authorised user he will be marked as present else absent. We are also generating an attendance report.
  • 4. Problem Statement To build an efficient security system which uses face recognition technique. Authenticating users based on their facial images which are compared against those present in the database. An alarm circuit that will be activated as soon as an intruder tries to break through the system.
  • 5. Modules in face recognition
  • 6. DESCRIPTION Sensor Module: It captures face images of individuals. Face Detection and feature extraction module: Human faces are detected and their features are extracted to form the template. Identification Module: The template extracted from the input image is compared against stored templates in the database. System Database Module: This module is responsible for enrolling users in a face recognition system database.
  • 7. EXTRACTION OF AN IMAGE Fig 2.a Feature extraction of an Image
  • 9. STEPS IN PCA ALGORITHM Organize the data set and calculate empirical mean: Data is organized into a set of matrices and empirical mean(U) is calculated. Calculate the deviations from the mean: A new matrix B stores the mean subtraction. This is used to minimize the mean square error. Find the covariance matrix : Covariance matrix C is computed by multiplying matrix B by its transpose.
  • 10. Find the eigenvectors and eigen values of the covariance matrix: The diagonal matrix D is calculated of the covariance matrix C. Rearrange the eigenvectors and eigen values : The eigen vector matrix V and eigen value matrix D is sorted in the order of decreasing eigen weights.
  • 11. Select a subset of the eigenvectors as basis vectors: Save the first L columns of V as the M L matrix W: Convert the source data to z-scores (optional): Create an M 1 standard deviation vector s from the square root of each element along the main diagonal of the diagonalized covariance matrix C. Project the z-scores of the data onto the new basis:
  • 13. WORKING IC mct2e receives the output from the computer via its First and Second pins. If the computer sends a high output i.e. +5Volts when the face does not match the LED inside the IC glows. This illumination of the LED is detected by the transistor which inturn sends the signal to the relay and turns it into an ON state. When low signal is sent by the computer then the low signal(0 Voltage) is sent by the computer to the IC mct2e and the Relay is switched off.
  • 15. WORKING When the Relay is on the current flows through the timer and the alarm circuit is active. If anyone touches the touch plate the buzzer rings and the intruder is detected. When the Relay is off the 555 timer is disconnected from the power supply and the circuit is deactivated.
  • 16. CONCLUSION Using Principle component analysis the face recognition was implemented using c# with the help of small set of images. The system performance depends on the images stored in the database and the input image provided by the user. This system identifies the users based on any facial images. The monthly attendance report for the employees was successfully generated.
  • 17. FUTURE SCOPE Techniques like 3-D modelling, neural networks can be used. Different circuits like door circuit, vibrator sensor circuit can be integrated with face recognition to fulfill different security objectives.
  • 18. REFERENCES http://encyclopedia.jrank.org/articles/pages/6741/Face -Recognition.html http://www.electronicsforyou.com http://www.howstuffworks.com http://www.nec.com/global/solutions/security/technol ogies/face-recognition.html