14-06-2012, 05:36 PM
FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS
abstract
Face Recognition is the process of identification of a person by their facial image. This technique
makes it possible to use the facial images of a person to authenticate him into a secure system,
for criminal identification, for passport verification,... Face recognition approaches for still
images can be broadly categorized into holistic methods and feature based methods . Holistic
methods use the entire raw face image as an input, whereas feature based methods extract local
facial features and use their geometric and appearance properties.
This paper describes how to build a simple, yet a complete face recognition system using
Principal Component Analysis, a Holistic approach. This method applies linear projection to the
original image space to achieve dimensionality reduction. The system functions by projecting
face images onto a feature space that spans the significant variations among known face images.
The significant features known as eigenfaces do not necessarily correspond to features such as
ears, eyes and noses. It provides for the ability to learn and later recognize new faces in an
unsupervised manner. This method is found to be fast, relatively simple, and works well in a
constrained environment.
Biometrics is automated technology
Biometrics is automated method of identifying a person or verifying the identity of a person
based on a physiological or behavioral characteristic. Examples of physiological characteristics
include hand or finger images, facial characteristics.
Biometric authentication requires comparing a registered or enrolled biometric sample (biometric
template or identifier) against a newly captured biometric sample (for example, captured image
during a login). During , as shown in the picture below, a sample of the biometric
trait is captured, processed by a computer, and stored for later comparison.Biometric recognition
can be used in mode, where the biometric system identifies a person from the
entire enrolled population by searching a database for a match based solely on the biometric.
Sometime identification is called "one-to-many" matching.
FACE RECOGNITION:
The identification of a person by their facial image can be done in a number of different
ways such as by capturing an image of the face in the visible spectrum using an inexpensive
camera or by using the infrared patterns of facial heat emission. Facial recognition in visible light
typically model key features from the central portion of a facial image. Using a wide assortment
of cameras, the visible light systems extract features from the captured image(s) that do not
change over time while avoiding superficial features such as facial expressions or hair. Several
approaches to modeling facial images in the visible spectrum are Principal Component Analysis,
Local Feature Analysis, neural networks, elastic graph theory, and multi-resolution analysis.
FINGERPRINTS:
Fingerprints are unique for each finger of a person including identical twins. One of the
most commercially available biometric technologies, fingerprint recognition devices for desktop
and laptop access are now widely available from many different vendors at a low cost. With
these devices, users no longer need to type passwords – instead, only a touch provides instant
access. Fingerprint systems can also be used in identification mode. Several states check
fingerprints for new applicants to social services benefits to ensure recipients do not fraudulently
obtain benefits under fake names.
IRIS RECOGNITION:
This recognition method uses the iris of the eye, which is the colored area that surrounds
the pupil. Iris patterns are thought unique. The iris patterns are obtained through a video-based
image acquisition system. Iris scanning devices have been used in personal authentication
applications for several years. Systems based on iris recognition have substantially decreased in
price and this trend is expected to continue. The technology works well in both verification and
identification modes (in systems performing one-to-many searches in a database). Current
systems can be used even in the presence of eyeglasses and contact lenses. The technology is not
intrusive. It does not require physical contact with a scanner. Iris recognition has been
demonstrated to work with individuals from different ethnic groups and nationalities.
SIGNATURE VERIFICATION:
This technology uses the dynamic analysis of a signature to authenticate a person. The
technology is based on measuring speed, pressure and angle used by the person when a signature
is produced. One focus for this technology has been e-business applications and other
applications where signature is an accepted method of personal authentication.
introduction
The face is our primary focus of attention in social intercourse, playing a major role in conveying
identity and emotion. We can recognize thousands of faces learned throughout our lifetime and
identify familiar faces at a glance after years of separation. This skill is quite robust, despite large
changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such
as glasses or changes in hairstyle or facial hair.
Computational models of face recognition, in particular, are interesting because they can
contribute not only to theoretical insights but also to practical applications. Computers that
recognize faces could be applied to a wide variety of problems, including criminal identification,
security systems, image and film processing, and human computer interaction. Unfortunately,
developing a computational model of face recognition is quite difficult, because faces are
complex, multidimensional, and meaningful visual stimuli.