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DIMENSIONALITY
REDUCTION IN PCA FOR
FACE RECOGNITION
Training set of face images
which is a subset of JNTU
Face Database ABSTRACT
Face recognition is an
advanced statistical
technology in computer
systems that can assist
recognition of human faces
using principal component
analysis (PCA) by
comparing the given facial
characteristics with the
already available data
(faces). Human faces are
normally upright so they
can be treated as 2-D
images rather than 3-D.
The 2-D facial image can be
converted into a 1-D
vector of pixels and
projected into the principal
components of the feature
space called the
eigenspace projection,
which is calculated from the
eigenvectors of the
covariance matrix derived
from a set of facial images.
The projections

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AUTOMATIC HUMAN FACE
RECOGNITION USING
RADON TRANSFORM Fig.1
Radon transform of the
image ABSTRACT This
paper presents a new
technique for face
recognition using Radon
transform. The technique
captures the facial
features from different
directions. Proposed
method which is robust to
zero mean additive white
noise derives local and
directional information of
the face images. The
technique acts as low pass
filter, enhancing low
frequency components
useful in face recognition
which improves the
recognition rate. The
images are classified by
using minimum distance 1
classifier. The feasibility of
Radon transform based
model has been
successfully tested using
ORL database. The
effectiveness of the
algorithm is shown in terms
of absolute performance
and compared

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Recognition of Partially
Occluded, Expression
Variant Faces using Elastic
Bunch Graph Matching
ABSTRACT-The aim of the
paper is to develop a
system able to process a
large number of new faces
by introducing a novel data
structure, namely face
graph (FG), which requires
less space for storing
important information of
face. The occluded image
and thereafter the
occluded part of the face is
detected using Elastic
Bunch Graph Matching
method. This procedure is
applicable for different

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