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
Eigen face which is eigen vectors of the set of faces, which may not correspond
to general facial features such as eyes, nose, and lips. The Eigen face
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 represents 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.
The system performs by projecting pre extracted face image onto a set of face space that represents 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:
The
Face is a complex multidimensional structure and needs good computing
techniques for recognition. The face is our primary and first focus of
attention in social life playing an important role in identity of individual.
We can recognize a number of faces learned throughout our lifespan and identify
that faces at a glance even after years. There may be variations in faces due
to aging and distractions like beard, glasses or change of hairstyles. Face
recognition is an integral part of biometrics. In biometrics basic traits of
human is matched to the existing data and depending on result of matching
identification of a human being is traced. Facial features are extracted and
implemented through algorithms which are efficient and some modifications are
done to improve the existing algorithm models. Computers that detect and
recognize faces could be applied to a wide variety of practical applications
including criminal identification, security systems, identity verification etc.
Face detection and recognition is used in many places nowadays, in websites
hosting images and social networking sites. Face recognition and detection can
be achieved using technologies related to computer science. Features extracted
from a face are processed and compared with similarly processed faces present
in the database. If a face is recognized it is known or the system may show a
similar face existing in database else it is unknown. In surveillance system if
a unknown face appears more than one time then it is stored in database for
further recognition. These steps are very useful in criminal identification. In
general, face recognition techniques can be divided into two groups based on
the face representation they use appearance-based, which uses holistic texture
features and is applied to either whole-face or specific regions in a face
image and feature-based, which uses geometric facial features (mouth, eyes,
brows, cheeks etc), and geometric relationships between them.
PRINCIPAL
COMPONENT ANALYSIS:
Fig.1.
Block Diagram of Face Recognition with PCA
Face
Image Representation:
Each
face is represented by
Feature
vector of a face is stored in a NxN matrix. Now, this two dimensional vector is changed to one dimensional
vector.
Mean
and Mean Centered Image:
Average
Face Image is calculated by
Covariance
Matrix
A
covariance matrix is constructed as:
Size of covariance matrix will be NxN (4x 4 in this case). Eigen vectors
corresponding to this covariance matrix is needed to be calculated, but that
will be a tedious task therefore, for simplicity we calculate A Transpose A which would be a 2 x 2 matrix in this case.
Recognition
Steps:
MATLAB Implementation:
Fig.2. MATLAB Main GUI
Fig.3. Data Base Store GUI
Fig.4. Face Matching with PCA GUI
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