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Abstract high accuracy and low intrusiveness. It

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Abstract

The reason of
that project is to implement a face recognition algorithm that is presented
here is a memory based face recognition system. Face recognition challenging
due to the Wide variety of faces and the complexity of noises and image
backgrounds. And the more familiar functionality of visual surveillance
systems. The goal of this project is to give a small background about face recognition
and how it works. I also show how humans going to use face recognition in the
future.

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Introduction

As one of the
most successful applications of image analysis and understanding, face
recognition has recently received significant attention, especially during the
few years. The strong need for user-friendly systems that can secure our assets
and protect our privacy without losing our identity in a sea of numbers is
obvious. At present, one needs a PIN to get cash from an ATM, a password for a
computer, a dozen others to access the internet. Although extremely reliable
methods of biometric personal identification exist. Face recognition is one of
the few biometric methods that possess the merits of both high accuracy and low
intrusiveness. It has the accuracy of a physiological approach without being
intrusive. For this reason, since the early 70’s (Kelly, 1970), face recognition
has drawn the attention of researchers in fields from security, psychology, and
image processing, to computer vision. Numerous algorithms have been proposed
for face recognition; for detailed survey please see Chellappa (1995) and Zhang
(1997). Biometrics
is the emerging area of bioengineering; it is the automated method of
recognizing person based on a physiological or behavioral characteristic. There
exist several biometric systems such as signature, finger prints, voice, iris,
retina, hand geometry, ear geometry, and face. Among these systems, facial
recognition appears to be one of the most universal, collectable, and
accessible systems.

 

What is face recognition

Facial recognition (or face recognition) is a
biometric method of identifying an individual by comparing live capture or
digital image data with the stored record for that person. Facial recognition
systems are commonly used for security purposes but are increasingly being used
in a variety of other applications. The Kinect motion gaming system, for
example, uses facial recognition to differentiate among players. Some mobile
payment systems use facial recognition to securely trust users, and facial
recognition systems are currently being studied or deployed for airport
security. Face recognition
in general and the recognition of moving people in natural scenes, require a set of visual tasks to be performed
robustly. Facial recognition systems based on faceprints
can quickly and accurately identify target individuals when the conditions are
favorable. However, if the subject’s face is partially obscured or in profile
rather than facing forward, or if the light is insufficient, the software is
less reliable. Nevertheless, the technology is evolving quickly and there are
several emerging approaches, such as 3D modeling,
that may overcome current problems with the systems. According to the National
Institute of Standards and Technology (NIST), the incidence of false positives in
facial recognition systems has been halved every two years since 1993 and, as
of the end of 2011, was just 0.003%

Currently,
a lot of facial recognition development is focused on smartphone applications. Smartphone facial
recognition capacities include image tagging and other social networking integration purposes as
well as personalized marketing. A research team at Carnegie Mellon has
developed a proof-of-concept iPhone app that can take a picture of an
individual and — within seconds — return the individual’s name, date of birth
and social security number. There are many
advantages associated with facial recognition. Compared to other biometric
techniques, facial recognition is of a non-contact nature. Face images can be
captured from a distance and can be analyzed without ever requiring any
interaction with the user/person. As a result, no user can successfully imitate
another person. Facial recognition can serve as an excellent security measure
for time tracking and attendance. Facial recognition is also cheap technology
as there is less processing involved, like in other biometric techniques.

 

How Facial Recognition Systems Work

 

As one of several methods of what are called “biometric”
identification systems, facial recognition examines physical features of a
person’s body to uniquely distinguish one person from all the others. Other
forms of this type of work include the very common fingerprint matching, retina
scanning, iris scanning (using a more readily observable part of the eye) and
even voice recognition.

These systems take in data – often an image –
from an unknown person, analyze the data in that input, and attempt to match
them to existing entries in a database of known people’s faces or voices.
Facial recognition does this in three steps: detection, faceprint creation, and
verification or identification.

When an image is captured, computer software
analyzes it to identify where the faces are in, say, a crowd of people. In a
mall, for example, security cameras will feed into a computer with facial
recognition software to identify faces in the video feed.

Once the system has identified any potential
faces in an image, it looks more closely at each one. Sometimes the image needs
to be reoriented or resized. A face very close to the camera may seem tilted or
stretched slightly; someone farther back from the camera may appear smaller or
even partially hidden from view.

When the software has arrived at a proper size
and orientation for the face, it looks even more closely, seeking to create
what is called a “faceprint.” Much like a fingerprint record, a faceprint is a
set of characteristics that, taken together, uniquely identify one person’s face.
Elements of a faceprint include the relative locations of facial features, like
eyes, eyebrows, and nose shape. A person who has small eyes, thick eyebrows and
a long narrow nose will have a very different faceprint from someone with large
eyes, thin eyebrows, and a wide nose. Eyes are a key factor in accuracy. Large
dark sunglasses are more likely to reduce the accuracy of the software than
facial hair or regular prescription glasses.

A faceprint can be compared with a single photo
to verify the identity of a known person, say an employee seeking to enter a
secure area. Faceprints can also be compared to databases of many images in
hopes of identifying an unknown person.

 

Example
of facial recognition

Face ID

Face ID a form
of biometric authentication. Rather than a password or a security
dongle or authentication app, biometrics are
something you are. Fingerprint recognition is also a biometric.

Instead of one
or more fingerprints, as with Touch ID, Face ID relies on the unique
characteristics of your face. Apple is betting that its technology can meet six
separate hurdles:

·        
Initially scan your face accurately enough to
recognize it later.

·        
Compare a new scan with the stored one with
enough flexibility to recognize you nearly all the time.

·        
Scan your face in a wide variety of lighting
conditions.

·        
Update your facial details as you age, change
hairstyles, grow a mustache, change your eyebrows, get plastic surgery, and so
forth to still recognize you.

·        
Let you wear hats, scarves, gloves, contact
lenses, and sunglasses, and still be recognized.

 

Face recognition advantages

 

The
software can be used for security purposes in organizations and in secured
zones.
The
software stores the faces that are detected and automatically marks
attendance.
The system
is convenient and secure for the users.
It saves
their time and efforts.
Inexpensive
technique of identification

 

Face recognition disadvantages

 

The system
doesn’t recognize properly in poor light so may give false results.
It can
only detect face from a limited distance.
Face
recognition systems can’t tell the difference between identical twins.

 

 

 

 

 

Facing the Future

What the Future Holds

Robots with facial recognition
technology, overall, can help cut costs, be assigned tasks that are otherwise
difficult or impractical for humans or in areas with a clear deficit of human
resource. They will also play an important role in services that require a high
level of accuracy. Mobile robots like those by SMP Robotics can be used in
restricted areas for patrolling. If the system recognizes a human in the PTZ
(Pan-Tilt-Zoom) camera image, it transmits an alarm signal to a guard station.
After analyzing the video image, an operator can decide to reset an alarm, turn
on a siren, or a turn on a strobe on the robot and send out security personnel
to deal with the intrusion. In large facilities and factories, such robot
surveillance can add an extra layer of security.

 

 

Face Recognition and the Future of Security

Sensors
transforming business

Businesses will
take advantage of face recognition to add another layer to their data security.
As facial recognition re-enters the smartphone arena, you should expect
companies to further adopt mobile technology in their business processes.
Currently, mobile devices have become a vital business tool in the enterprise,
especially in keeping tabs with offshore, mobile, and field-based workers.

In fact, some
businesses have initiated the use of smartphones and tablets in managing
employees’ hours of service and duty status to replace paper-based reports.

For
example: The Federal
Motor Carrier Safety Administration (FMCSA) announced the final ruling to bring
in the Electronic Logging Device (ELD) that will affect drivers and fleet
operators across the United States. In a post about ELD compliance by
Fleetmatics, mobile gadgets will be
used by drivers in logging their hours automatically while their managers will
receive real-time reports about their field employees, as well as being sent
alerts to prevent violations. With the facial recognition sensor technology on
mobile devices, this type of data will be kept safe and secure due to this
extra layer of security.

 

CONCLUSION

Face recognition
system presented in this term-paper contributes a resilient face recognition
model based on the mapping of behavioral characteristics with the physiological
bio-metric characteristics. looking at the developments in facial recognition
over the recent years, we could be looking at people using facial recognition
to operate most thing in there day to day life such as TV and also they could
use face recognition integrated with house hold security system. 

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