16-01-2014, 01:07 PM
Face Recognition Using PCA and Eigen Face Approach
[attachment=59930]
Abstract
Face is a complex multidimensional structure and needs a good computing
techniques for recognition.
Our approach treats face recognition as a
two-dimensional recognition problem. In this scheme face recognition is done by
Principal Component Analysis (PCA). Face images are projected onto a face space
that encodes best variation among known face images. The face space is defined
by eigenface which are eigenvectors of the set of faces, which may not correspond
to general facial features such as eyes, nose, lips. The eigenface approach uses
the PCA for recognition of the images. The system performs by projecting pre
extracted face image onto a set of face space that represent significant variations
among known face images. Face will be categorized as known or unknown face
after matching with the present database. If the user is new to the face recognition
system then his/her template will be stored in the database else matched against
the templates stored in the database. The variable reducing theory of PCA
accounts for the smaller face space than the training set of face.
Introduction
Biometrics
Biometrics is used in the process of authentication of a person by verifying or
identifying that a user requesting a network resource is who he, she, or it claims
to be, and vice versa. It uses the property that a human trait associated with a
person itself like structure of finger, face details etc. By comparing the existing
data with the incoming data we can verify the identity of a particular person [1].
There are many types of biometric system like fingerprint recognition, face
detection and recognition, iris recognition etc., these traits are used for human
identification in surveillance system, criminal identification. Advantages of using
these traits for identification are that they cannot be forgotten or lost. These are
unique features of a human being which is being used widely [2].
Principal Component Analysis (PCA)
Principal component analysis (PCA) was invented in 1901 by Karl Pearson. PCA
is a variable reduction procedure and useful when obtained data have some
redundancy. This will result into reduction of variables into smaller number of
variables which are called Principal Components which will account for the most
of the variance in the observed variable.
Problems arise when we wish to perform recognition in a high-dimensional
space. Goal of PCA is to reduce the dimensionality of the data by retaining
as much as variation possible in our original data set.
On the other hand
dimensionality reduction implies information loss. The best low-dimensional space
can be determined by best principal components.
[attachment=59930]
Abstract
Face is a complex multidimensional structure and needs a good computing
techniques for recognition.
Our approach treats face recognition as a
two-dimensional recognition problem. In this scheme face recognition is done by
Principal Component Analysis (PCA). Face images are projected onto a face space
that encodes best variation among known face images. The face space is defined
by eigenface which are eigenvectors of the set of faces, which may not correspond
to general facial features such as eyes, nose, lips. The eigenface approach uses
the PCA for recognition of the images. The system performs by projecting pre
extracted face image onto a set of face space that represent significant variations
among known face images. Face will be categorized as known or unknown face
after matching with the present database. If the user is new to the face recognition
system then his/her template will be stored in the database else matched against
the templates stored in the database. The variable reducing theory of PCA
accounts for the smaller face space than the training set of face.
Introduction
Biometrics
Biometrics is used in the process of authentication of a person by verifying or
identifying that a user requesting a network resource is who he, she, or it claims
to be, and vice versa. It uses the property that a human trait associated with a
person itself like structure of finger, face details etc. By comparing the existing
data with the incoming data we can verify the identity of a particular person [1].
There are many types of biometric system like fingerprint recognition, face
detection and recognition, iris recognition etc., these traits are used for human
identification in surveillance system, criminal identification. Advantages of using
these traits for identification are that they cannot be forgotten or lost. These are
unique features of a human being which is being used widely [2].
Principal Component Analysis (PCA)
Principal component analysis (PCA) was invented in 1901 by Karl Pearson. PCA
is a variable reduction procedure and useful when obtained data have some
redundancy. This will result into reduction of variables into smaller number of
variables which are called Principal Components which will account for the most
of the variance in the observed variable.
Problems arise when we wish to perform recognition in a high-dimensional
space. Goal of PCA is to reduce the dimensionality of the data by retaining
as much as variation possible in our original data set.
On the other hand
dimensionality reduction implies information loss. The best low-dimensional space
can be determined by best principal components.