Biometrics refers to metrics related to human characteristics. Biometrics authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance.

1. Biometrics
- face recognition

2. INTRODUCTION
 Everyday action are increasingly being handled electronically, instead of pencil and paper
or face to face.
 This growth in electronic transaction result in great demand for fast and accurate user
identification and authentication.
 Access codes for buildings, banks account and computer system often use PIN for
identification and security clearances.
 Using the proper PIN gain access, but the user of the pin is not verified. When credit or
ATM card is lost or stolen, an unauthorized user can often come up with correct personal
codes.
 Face recognition technology may solve this problem since a face is undeniably
connected to its owner except in case of identical twins.

3. BIOMETRIC
 A biometric is a unique, measurable characteristic of a human being that can be used to
automatically recognize an individual or verify an individual identity.
CHARACTERISTICS TEMPLATES
011001010010101…
011010100100110…
001100010010010...

4. TYPES OF BIOMETRIC
 PHYSIOLOGICAL BIOMETRICS
(based on measurements and data
derived from direct the human
body) include:
 a. Finger-scan
 b. Facial Recognition
 c. Iris-scan
 d. Retina-scan
 e. Hand-scan.
 BEHAVIORAL BIOMETRICS
(based on measurements and data
derived from an action) include:
 a. Voice-scan
 b. Signature-scan
 c. Keystroke-scan
Biometrics can measure both physiological and behavioral
characteristics.

5. STEPS OF AUTHENTICATION
 All identification or authentication technologies operate using the
following four stages:
 CAPTURE: A physical sample is captured by the system during enrollment
and also in identification or Verification process.
 EXTRACTION: unique data is extracted from the sample and a template is
created.
 COMPARISON: the template is then compared with a new sample.
 MATCH/NON MATCH: the system decides if the features extracted from
the new samples are a match or a non match.

6. Authenticate:
Database
Enrollment subsystem
Biometric
reader
Feature
Extractor
Authentication subsystem
Biometric
reader
Feature
Extractor
Biometric
Matcher
Enroll:
Match or
No Match
1010010…
Template
1010010…
Template
TYPICAL BIOMETRIC AUTHENTICATION WORKFLOW

7. FACIAL RECOGNITION
 Facial recognition (or face recognition) is a type of biometric software
application that can identify a specific individual in a digital image by
analyzing and comparing patterns.
 Facial recognition systems are commonly used for security purposes but
are increasingly being used in a variety of other applications. For example,
Facebook uses facial recognition software to help automate user tagging
in photographs.

8. For face recognition there are two
types of comparisons.
VERIFICATION is where the system
compares the given individual with
who that individual says they are and
gives a yes or no decision
IDENTIFICATION is where the system
compares the given individual to all
the Other individuals in the database
and gives a ranked list of matches.

9. Identification (1:N)
Biometric
reader
Biometric
Matcher
Database
Verification (1:1)
Biometric
reader
Biometric
Matcher
ID
Database
This person is
Emily Dawson
Match
I am Emily
Dawson
IDENTIFICATION VS VERIFICATION

10. HOW FACE RECOGNITION SYSTEMS WORK
 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. 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
 Cheekbones
 Jaw line
 Chin
These nodal points are measured to create a numerical code, a string of numbers that represents
a face in the database. This code is called face print. Only 14 to 22 nodal points are needed for
software to complete the recognition process.

11. IMPLEMENTATION OF FACE RECOGNITION
TECHNOLOGY
 The implementation of face recognition technology includes
the following four stages:
1. Data acquisition
2. Input processing
3. Face image classification
4. Decision making .

12. 1.DATA ACQUISITION
 Facial scan technology can acquire faces from
almost any static camera or video system that
generate images of sufficient quality and
resolution.
 High quality enrollment is essential to eventual
verification and identification enrollment
images define the facial characteristics to be
used in all future authentication events.

14. 2.INPUT PROCESSING
 Images are cropped such that the ovoid facial image remain
and color images are normally converted to black and white
in order to facilitate initial comparisons based on grayscale
characteristics.
 First the presence of faces or face in 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 the one
on the template.

15. 3.FACE IMAGE CLASSIFICATION
 All facial scan system attempt to match visible facial features in a fashion
to the way people recognize one another.
 The features most utilized are those least likely to change significantly
over time:
upper ridges of eye sockets, areas around the cheekbones, sides of the
mouth, nose shape, and position of major features relative to each other.

17. 4.DECISION MAKING
 Enrollment templates are normally created from a multiplicity of
processed facial images.
 These template can vary in size from less than 100 bytes, generated
through certain vendors and to over 3K for templates.
 It compares match template against enrollment template.
 A series of images is acquired and scored against the enrollment,
so that a user attempting 1:1 verification within a facial scan system
may have 10 to 20 match attempts take place within 1 to 2
seconds.

18. SOFTWARES
 Facial recognition software falls into a larger group of technologies
known as biometrics. Facial recognition methods may vary, but they
generally involve a series of steps that serve to capture, analyze and
compare your face to a database of stored images.
 The basic process that is used by the Facial system to capture and
compare images:
1. Detection
2. Alignment
3. Normalization
4. Representation
5.Matching.

19.  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 second. 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 degree to the
camera for the system to register it.
 NORMALIZATION: the image of the head a 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 the distance of the
camera. Light does not impact the normalization process.

20.  REPRESENTATION: the system translate the facial data into a unique code.
The 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 linked to at least one stored facial representation.

21. FACIAL RECOGNITION ALGORITHM
 2D Eigen face
Principle Component Analysis (PCA)
 3D Face Recognition
3D Expression Invariant Recognition
3D Morphable Model

22. FACIAL RECOGNITION: EIGENFACE
 Decompose face images into a small
set of characteristic feature images.
 A new face is compared to these
stored images.
 A match is found if the new faces is
close to one of these images.

23. FACIAL RECOGNITION : PCA –TRAINING SET

24. FACIAL RECOGNITION: PCA TRAINING
 Find average of training images.
 Subtract average face from each
image.
 Create covariance matrix.
 Generate Eigenfaces.
 Each original image can be
expressed as a linear combination
of the Eigenfaces – facespace.

25. FACIAL RECOGNITION: PCA RECOGNITION
 A new image is project into the “facespace”.
 Create a vector of weights that describes this image.
 The distance from the original image to this Eigenfaces is
compared.
 If within certain thresholds then it is a recognized face.

26. FACIAL RECOGNITION: 3D EXPRESSION INVARIANT
RECOGNITION
 Treats face as a deformable object.
 3D system maps a face.
 Captures facial geometry in canonical
form.
 Can be compared to other canonical
forms.

27. FACIAL RECOGNITION: 3D MORPHABLE MODEL
 Create a 3D face model
from 2D images.
 Synthetic facial images
are created to add to
training set.
 PCA can then be done
using these images.
The figure shows an
application of our
approach. Matching a
Morphable model
automatically to a single
sample image (1) of a
face results in a 3D
shape (2) and a texture
map estimate. The
texture estimate can be
improved by additional
texture extraction (4).
The 3D model is
rendered back into the
image after changing
facial attributes, such as
gaining (3) and loosing
weight (5), frowning (6),
or being forced to smile
(7).

28. PERFORMANCE
 1. False rejection rates (FRR) : The probability that a system will
fail to identify an enrollee. It is also called type 1 error rate.
FRR= NFR/NEIA Where FRR= false rejection rates NFR=
number of false rejection rates NEIA= number of enrollee
identification attempt
 2. False acceptance rate (FAR) : The probability that a system
will incorrectly identify an individual or will fail to reject an
imposter. It is also called as type 2 error rate FAR= NFA/NIIA
Where FAR= false acceptance rate NFA= number of false
acceptance NIIA= number of imposter identification attempts

29. ADVANTAGES AND DISADVANTAGES
 Advantages :
1. There are many benefits to face recognition systems such as its
convenience and Social acceptability. All you need is your picture taken
for it to work.
2. Face recognition is easy to use and in many cases it can be
performed without a Person even knowing.
3. Face recognition is also one of the most inexpensive biometric in the
market and Its price should continue to go down.
 Disadvantage:
1. Face recognition systems cant tell the difference between identical
twins.

30. APPLICATIONS
 There are numerous applications for face recognition technology:
Commercial Use:
 Day Care: Verify identity of individuals picking up the children.
 Residential Security: Alert homeowners of approaching personnel
 Voter verification: Where eligible politicians are required to verify
their identity during a voting process.
 Banking using ATM: The software is able to quickly verify a
customer.

31. 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 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.

32. THANK YOU