Please open this ppt in ms Office 2013 or higher versions to see graphical effects

1. Head of department
-Sabina Ansari
Case study teacher
-Priyanka pawar
LAXMAN DEVRAM SONAWANE
COLLEGE

2. Case study
on
Face recognition in
e-attendance

4. Overview
Introduction History
What are
biometrics
Why we
choose face
recognition
over other
biometrics
What is face
recognition
Components
of face
recognition
How facial
recognition
system works
Where face
recognition
technology is
used
Future of face
recognition

5. Introduction
• In today's networked world, the need to maintain the security of
information or physical property is becoming both increasingly
important and increasingly difficult.
• Face recognition is one of the few biometric methods that possess the
merits of both high accuracy & Complex and largely software based
technique.
• Analyze unique shape, pattern & positioning of facial features.
• It compare scans to records stored in central or local database or
even on a smart card.

6. History
• The first attempts to use face recognition began in the 1960’s with a
semi-automated system.
• Marks were made on photographs to locate the major features; it
used features such as eyes, ears, noses, and mouths.
• Then distances and ratios were computed from these marks to a
common reference point and compared to reference data.
• In 1970s, Goldstein and Harmon used 21 specific subjective markers
such as hair color and lip thickness to automate the recognition.
• This proved even harder to automate due to the subjective nature of
many of the measurements still made completely by hand.

7. What are biometrics
• A biometric is a unique, measurable
characteristic of a human being that
Can be used to automatically recognize
an individual or verify an individual’s
identity.

8. Biometrics can measure both physiological and behavioral
characteristics –
• Physiological biometrics:- This types of biometrics is based on
measurements and data derived from direct measurement of a part
of the human body.
• Behavioral biometrics:- This types of biometrics is based on
measurements and data derived from an action.

9. Types of biometrics
PHYSIOLOGICAL BEHAVIORAL
 Finger-scan Voice-scan
 Facial Recognition Signature-scan
 Iris-scan Keystroke-scan
 Retina-scan
 Hand-scan

10. Why we choose face recognition over other biometrics
• It requires no physical interaction on behalf of user.
• It is accurate and allows for high enrolment and verification.
• Not require an expert to interpret the comparison result.
• It can use your existing hardware infrastructure, existing cameras and
image capture, devices will work with no problems.
• It is the only biometric that allow you to perform passive identification
in one to Many environments (e.g.: identifying a terrorist in a busy
Airport terminal.

11. What is face recognition
• Face recognition is one of the few
biometric methods that possess the
merits of both high accuracy and low
intrusiveness.
• A facial recognition system is a computer application for
automatically identifying or verifying a person from a digital
image from the source, One of the ways to do this is by comparing
selected facial features from the image and a facial database.

12. • Detection – two-class classification.
• Face vs. Non-face.
• Recognition – multi-class classification.
• One person vs. all the others.
Difference between face detection and recognition

13. Two types of comparison in face recognition
• Face Verification: The system compares a face image that might not
belong to the database, verify whether it is from the person it is
claimed to be in the database.
• Face Identification: The system compares a face image that belongs
to a person in a database, tell whose image it is.

14. Stages of identification
Capture
Extraction
Comparison
Match/Non match
Accept/Project
1
2
3
4
5
Capture- Capture the behavioral
and physical sample.
Extraction- Unique data is extracted
from the sample and a template is
created.
Comparison- The template is
compared with a new sample.
Match/non match- The system
decides whether the new samples
are matched or not.

15. Components of face recognition
 Enrollment module-An automated
mechanism that scans and
captures a digital or analog
image of a living personal
characteristics.
 Database-Another entity which
handles compression ,processing
,data storage and compression of
the captured data with stored
data.
 Identification module-The third
interfaces with the application
system.

16. How facial recognition system works
• Facial recognition software is based on the ability to first recognize
faces, which is a technological feat in itself. If you look at the mirror,
you can see that your face has certain distinguishable landmarks.
These are the peaks and valleys that make up the different facial
features.
• VISIONICS defines these landmarks as nodal points. There are about
80 nodal points on a human face.

17. • distance between the eyes
• width of the nose
• depth of the eye socket
• cheekbones
• jaw line
Nodal points that are measured by the software

18. • Detection- when the system is attached to a video surveillance
system, the recognition software searches the field of view of a
video camera for faces. If there is a face in the view, it is detected
within a fraction of a second. A multi-scale algorithm is used to
search for faces in low resolution. The system switches to a high-
resolution search only after a head-like shape is detected.
• Alignment- Once a face is detected, the system determines the
head's position, size and pose. A face needs to be turned at least 35
degrees toward the camera for the system to register it.
• Normalization-The image of the head is scaled and rotated so that it
can be registered and mapped into an appropriate size and pose.
Normalization is performed regardless of the head's location and
distance from the camera. Light does not impact the normalization
process.

19. • Representation-The system translates the facial data into a unique
code. This coding process allows for easier comparison of the newly
acquired facial data to stored facial data.
• Matching- The newly acquired facial data is compared to the stored
data and (ideally) linked to at least one stored facial representation.

20. Advantages and disadvantages
ADVANTAGES
• Convenient, social acceptability.
• More user friendly.
• Inexpensive technique of identification.
DISADVANTAGES
• Problem with false rejection when people change their hair style, grow
or shave a beard or wear glasses.
• Face recognition systems can’t tell the difference between identical
twins.

21. Where face recognition technology is used
• Airports and railway stations
• Voter verification
• Cashpoints
• Stadiums
• Public transportation
• Financial institutions
• Government offices
• Businesses of all kinds

22. Future of face recognition
• Some consider the problem impossible.
• Advancements in hardware and software.
• Slow integration into society in limited environments.
• Very large potential market.

23. CONCLUSION
• Face recognition technologies have been associated generally with
very costly top secure applications. Today the core technologies have
evolved and the cost of equipment's is going down dramatically due
to the integration and the increasing processing power. Certain
applications of face recognition technology are now cost effective,
reliable and highly accurate. As a result there are no technological or
financial barriers for stepping from pilot project to widespread
deployment.
• For implementations where the biometric system must verify and
identify users reliably over time, facial scan can be a very difficult, but
not impossible, technology to implement successfully.

24. Thanks