2. 1.Introduction
2. Facial recognition system
3. Facial Recognition
4. 2 face recognition
5. 3 face recognition
6. Implementation
7. How it works
8. Advantages & Disadvantage s
9. Applications
10. Conclusion
3. 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.
4. Access codes for buildings, banks accounts
and computer systems often use PINs for
identification and security clearances. 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
expect in the case of identical twins.
5. A facial recognition system is a computer
application capable of identifying or verifying a
person from a digital image or a video frame from
a video source. One of the ways to do this is by
comparing selected facial features from the image
and a facial database
6. 1.It requires no physical interaction on
behalf of the user.
2. It is accurate and allows for high
enrolment and verification rates.
3. It can use existing hardware
infrastructure, existing cameras and
image capture Devices will work with
no problems.
7. In the past, facial recognition software has relied on a 2D image to
compare or identify another 2D image from the database. To be
effective and accurate, the image captured needed to be of a face
that was looking almost directly at the camera, with little variance
of light or facial expression from the image in the database. This
created quite a problem.
8. The problems which still present problems to 2D
face recognition systems are:
1.Illumination
2.Pose
3.Expression
4.Occlusion
5.Ageing
9. A newly-emerging trend in facial recognition
software uses a 3D model, which claims to
provide more accuracy. Capturing a real-time 3D
image of a person's facial surface, 3D facial
recognition uses distinctive features of the face
-- where rigid tissue and bone is most apparent
, such as the curves of the eye socket, nose and
chin -- to identify the subject. These areas are all
unique and don't change over time.
Using depth and an axis of measurement that is
not affected by lighting, 3D facial recognition
can even be used in darkness and has the ability
to recognize a subject at different view angles
with the potential to recognize up to 90 degrees
(a face in profile).
10. 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. ( one to one)
IDENTIFICATION- The system compares the
given individual to all the Other individuals in
the database and gives a ranked list of matches
(one to many).
11. 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.
12. The implementation of 3d face recognition
technology includes the following five stages:
• Image acquisition
• Image processing
• Distinctive characteristic location
• Template creation
• Template matching
13. • 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.
14. 1. 3D Laser Scanners: The acquisition itself is via laser stripe and triangulation.
2. Stereo: . to use two images of an object or scene taken from slightly different
viewpoints.
3. Structured Light:
4. Time of Flight : measuring or inferring the distance that a beam of reflected light
has travelled.
5. Shape from Shading (Photoclinometry) : uses shading from an individual image
in order to estimate the surface orientation.
6. Photometric Stereo (PS): It constructs a 3D form from three or more images of
the same object each lit from a different and known direction and estimating surface
normal at each pixel coordinate
.
15. • 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.
• 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.
16. 1. All facial-scan systems attempt to match visible
facial features in a fashion similar to the way
people recognize one another.
2. 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.
17. 3. Behavioral changes such as alteration of
hairstyle, changes in makeup, growing or
shaving facial hair, adding or removing
eyeglasses are behaviors that impact the ability
of facial-scan systems to locate distinctive
features, facial-scan systems are developed to
the point where they can overcome such
variables
18. • Enrollment templates are normally created from a
multiplicity of processed facial images.
• These templates can vary in size from less than
100 bytes, generated through certain vendors and to
over 3K for templates.
• The 3K template is by far the largest among
technologies considered physiological biometrics.
• Larger templates are normally associated with
behavioral biometrics
19. • 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.
20.
Every face has numerous, distinguishable landmarks, the different peaks and
valleys that make up facial features. these landmarks defined as nodal points.
Each human face has approximately 80 nodal points. Some of these measured by
the software are:
Distance between the eyes
Width of the nose
Depth of the eye sockets
The shape of the cheekbones
The length of the jaw line.
Chin
These nodal points are measured creating a numerical code, called a faceprint,
representing the face in the database.
21. Detection
Acquiring an image can be accomplished by digitally scanning an existing
photograph (2D) or by using a video image to acquire a live picture of a
subject (3D).
Alignment
Once it detects a face, the system determines the head's position, size and
pose. As stated earlier, the subject has the potential to be recognized up to
90 degrees, while with 2D, the head must be turned at least 35 degrees
toward the camera.
22. Measurement
The system then measures the curves of the face on a sub-millimeter (or
microwave) scale and creates a template.
Representation
The system translates the template into a unique code. This coding gives
each template a set of numbers to represent the features on a subject's
face.
23. Matching
1. If the image is 3D and the database contains 3D images, then
matching will take place without any changes being made to the
image.
2. If the databases is still in 2D images.
When a 3D image is taken, different points (usually three) are
identified. For example, the outside of the eye, the inside of
the eye and the tip of the nose will be pulled out and measured.
3. Once those measurements are in place, an algorithm (a step-by-
step procedure) will be applied to the image to convert it to a 2D
image.
4. After conversion, the software will then compare the image with the
2D images in the database to find a potential match.
24. Verification or Identification
1. In verification, an image is matched to only one
image in the database (1:1). For example, an image taken
of a subject may be matched to an image in the database to
verify the subject is who he says he is.
2. If identification is the goal, then the image is compared to
all images in the database resulting in a score for each
potential match (1:N).
25.
26. 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.
27. Changes in acquisition environment reduce
matching accuracy.
Changes in physiological characteristics reduce
matching accuracy.
It has the potential for privacy abuse due to non
cooperative enrollment and identification
capabilities.
complexity of the sensor
28. Security/Counterterrorism. Access control,
comparing surveillance images to Know terrorist.
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 this is intended to stop voting where the
vote may not go as expected.
Banking using ATM: The software is able to
quickly verify a customer’s face.
29. • 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.
The level of interest in 3D face recognition is high among
biometric techniques. 3D face recognition has potential to
be a strong biometric.
