Face recognigion system ppt

Submitted To: Submitted By:
Prof. Sandeep Santosh Kashish Garg
1130319
Face Recognition

Contents
 Introduction
 History
 Facial Recognition
 Implementation
 How it works
 Strengths & Weaknesses
 Applications
 Advantages
 Disadvantages
 Conclusion
 References

Introduction
 Everyday actions are increasingly being
handled electronically, instead of pencil and
paper or face to face.
 This growth in electronic transactions results
in great demand for fast and accurate user
identification and authentication.
 Access codes for buildings, banks accounts and
computer systems often use PIN's for
identification and security clearances.

Contd…
 Using the proper PIN gains access, but the
user of the PIN is not verified. When
credit and ATM cards are lost or stolen, an
unauthorized user can often come up
with the correct personal codes.
 Face recognition technology may solve
this problem since a face is undeniably
connected to its owner except in the case
of identical twins.

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 your existing hardware
infrastructure, existing cameras and image
capture Devices will work with no
problems

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.

Facial Recognition
In Facial recognition there are two types
of comparisons:-
 VERIFICATION- The system compares
the given individual with who they say
they are and gives a yes or no
decision.
 IDENTIFICATION- The system
compares the given individual to all
the Other individuals in the database
and gives a ranked list of matches.

Contd…
 All identification or authentication technologies
operate using the following four stages:
 Capture: A physical or behavioral 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.

Implementation
The implementation of face recognition
technology includes the following four stages:
• Image acquisition
• Image processing
• Distinctive characteristic location
• Template creation
• Template matching

Image acquisition
• Facial-scan technology can acquire faces from
almost any static camera or video system that
generates 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.

Image Processing
Images are cropped such that the ovoid facial image
remains, 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 a 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.

Distinctive characteristic
location
 All facial-scan systems attempt to match
visible facial features in a fashion similar to
the way people recognize one another.
 The features most often utilized in facial-
scan systems are those least likely to
change significantly over time: upper ridges
of the eye sockets, areas around the
cheekbones, sides of the mouth, nose
shape, and the position of major features
relative to each other.

Contd..
 Behavioural changes such as alteration of
hairstyle, changes in makeup, growing or
shaving facial hair, adding or removing
eyeglasses are behaviours that impact the
ability of facial-scan systems to locate
distinctive features, facial-scan systems are not
yet developed to the point where they can
overcome such variables.

Template matching
• It compares match templates against enrollment
templates.
• 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.
• facial-scan is not as effective as finger-scan or
iris-scan in identifying a single individual from a
large database, a number of potential matches
are generally returned after large-scale facial-
scan identification searches.

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.

Contd..
Here are few nodal points that are
measured by the software.
1. distance between the eyes
2. width of the nose
3. depth of the eye socket
4. cheekbones
5. jaw line
6. chin

SOFTWARE
 Detection- when the system is attached to a
video surveilance 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.

Contd…
 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.
 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.

 The system maps the face and creates a
faceprint, a unique numerical code for that
face. Once the system has stored a
faceprint, it can compare it to the
thousands or millions of faceprints stored in
a database.
 Each faceprint is stored as an 84-byte file.

Strengths
 It has the ability to leverage existing image
acquisition equipment.
 It can search against static images such as
driver’s license photographs.
 It is the only biometric able to operate without
user cooperation.

Weaknesses
 Changes in acquisition environment reduce
matching accuracy.
 Changes in physiological characteristics reduce
matching accuracy.
 It has the potential for privacy abuse due to
noncooperative enrollment and identification
capabilities.

Applications
Replacement of PIN, physical tokens
No need of human assistance for identification
Prison visitor systems
Border control
Voting system
Computer security
Banking using ATM
Physical access control of buildings ,areas etc.

Advantages
• Convenient, social acceptability
• Easy to use
• Inexpensive technique of identification

Disadvantage
• Problem with false rejection when people
change their hair style, grow or shave a beard
or wear glasses.
• Identical twins

Conclusion
• Factors such as environmental changes
and mild changes in appearance impact
the technology to a greater degree
than many expect.
• 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.

References
• www.google.com
• www.wikipedia.com

Face recognigion system ppt

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Face recognigion system ppt

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