Thesis Chapters by Girish Gn
Face recognition is an emergent research area, spans over multiple disciplines such
as image pro... more Face recognition is an emergent research area, spans over multiple disciplines such
as image processing, computer vision and signal processing, machine learning. Face
recognition is mainly used for identity authentication/identification, security access
control and intelligent human-computer interaction.
This work compares holistic and hybrid face recognition methods. The hybrid face
feature extraction in which local features were derived using Multi Scale Block Local
Binary Patterns (MB-LBP) and global features are derived using Principal Component
Analysis (PCA). For a facial image a spatially enhanced, concatenated representation
is obtained by deriving a histogram from each grid that an input image
is divided. These histograms were projected to lower dimensions by applying PCA
to create eigenfaces. The holistic face representation of a subject was derived by
projecting several images of the subject into lower dimensions applying PCA.
There are several parameters affect the performance of local descriptor based face
recognition system viz: image size, grid size, operator scale and available codes. Impact
of these parameters on performance of FRS are not explored much in literature.
This thesis aims to study empirically the effect of these parameters for FRS configured
in verification mode.
Papers by Girish Gn
Face recognition is an emergent research area, spanning over multiple disciplines such as image p... more Face recognition is an emergent research area, spanning over multiple disciplines such as image processing, computer vision and signal processing. Moreover, face recognition is also used for identity authentication, security access control and intelligent human-computer interaction. This work compares face recognition methods using local features and global features. The local features were derived using Multi Scale Block Local Binary Patterns (MB-LBP) and global features are derived using Principal Component Analysis (PCA). For each facial image a spatially enhanced, concatenated representation was obtained by deriving a histogram from each grid of the divided input image. These histograms were projected to lower dimensions by applying PCA which represents local features to characterize the face of a subject. The global face representation of a subject was derived by projecting several images of the subject into lower dimensions applying PCA. Face Recognition was performed with different similarity metrics on ORL, JAFFE and INDIAN face databases and compared with other works. It was found that the local features (MB-LBP) are better than the global features (PCA) for face recognition.
Face recognition is one of the challenging research area of face image analysis. It is
well know... more Face recognition is one of the challenging research area of face image analysis. It is
well known that Local Binary Pattern (LBP) based face recognition system’s performance depends
on number of grids and scale of the operator. The effect of the large scale operators together with
the number of grids and the dependency on labeled binary codes are not explored much. This paper
aims to study empirically the effect of number of available binary codes, large scale operator and
number of grids for face verification. Face verification experiments were conducted in which Principal
Component Analysis (PCA) is applied to reduce the dimensionality of the features extracted using
Multi-scale Block LBP (MB-LBP). The experiments were evaluated using ORL, JAFFE and INDIAN
benchmark face recognition databases. It is observed that number of available binary labeled codes
decreases as the scale of the operator and number of grids are increased when MB-LBP is convoluted
with traditional convolution techniques. Moreover, it is noticed that increase in operator scale would
decreases the number of available codes which in turn affects the performance. In order to negate
this effect, this paper proposes to use Modified Convolution (MC) technique to increase the available
binary labeled codes. Experiments on benchmark databases suggested that use of modified convolution method would increase the available codes which in turn increases the performance of the face
verification system.
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Thesis Chapters by Girish Gn
as image processing, computer vision and signal processing, machine learning. Face
recognition is mainly used for identity authentication/identification, security access
control and intelligent human-computer interaction.
This work compares holistic and hybrid face recognition methods. The hybrid face
feature extraction in which local features were derived using Multi Scale Block Local
Binary Patterns (MB-LBP) and global features are derived using Principal Component
Analysis (PCA). For a facial image a spatially enhanced, concatenated representation
is obtained by deriving a histogram from each grid that an input image
is divided. These histograms were projected to lower dimensions by applying PCA
to create eigenfaces. The holistic face representation of a subject was derived by
projecting several images of the subject into lower dimensions applying PCA.
There are several parameters affect the performance of local descriptor based face
recognition system viz: image size, grid size, operator scale and available codes. Impact
of these parameters on performance of FRS are not explored much in literature.
This thesis aims to study empirically the effect of these parameters for FRS configured
in verification mode.
Papers by Girish Gn
well known that Local Binary Pattern (LBP) based face recognition system’s performance depends
on number of grids and scale of the operator. The effect of the large scale operators together with
the number of grids and the dependency on labeled binary codes are not explored much. This paper
aims to study empirically the effect of number of available binary codes, large scale operator and
number of grids for face verification. Face verification experiments were conducted in which Principal
Component Analysis (PCA) is applied to reduce the dimensionality of the features extracted using
Multi-scale Block LBP (MB-LBP). The experiments were evaluated using ORL, JAFFE and INDIAN
benchmark face recognition databases. It is observed that number of available binary labeled codes
decreases as the scale of the operator and number of grids are increased when MB-LBP is convoluted
with traditional convolution techniques. Moreover, it is noticed that increase in operator scale would
decreases the number of available codes which in turn affects the performance. In order to negate
this effect, this paper proposes to use Modified Convolution (MC) technique to increase the available
binary labeled codes. Experiments on benchmark databases suggested that use of modified convolution method would increase the available codes which in turn increases the performance of the face
verification system.
as image processing, computer vision and signal processing, machine learning. Face
recognition is mainly used for identity authentication/identification, security access
control and intelligent human-computer interaction.
This work compares holistic and hybrid face recognition methods. The hybrid face
feature extraction in which local features were derived using Multi Scale Block Local
Binary Patterns (MB-LBP) and global features are derived using Principal Component
Analysis (PCA). For a facial image a spatially enhanced, concatenated representation
is obtained by deriving a histogram from each grid that an input image
is divided. These histograms were projected to lower dimensions by applying PCA
to create eigenfaces. The holistic face representation of a subject was derived by
projecting several images of the subject into lower dimensions applying PCA.
There are several parameters affect the performance of local descriptor based face
recognition system viz: image size, grid size, operator scale and available codes. Impact
of these parameters on performance of FRS are not explored much in literature.
This thesis aims to study empirically the effect of these parameters for FRS configured
in verification mode.
well known that Local Binary Pattern (LBP) based face recognition system’s performance depends
on number of grids and scale of the operator. The effect of the large scale operators together with
the number of grids and the dependency on labeled binary codes are not explored much. This paper
aims to study empirically the effect of number of available binary codes, large scale operator and
number of grids for face verification. Face verification experiments were conducted in which Principal
Component Analysis (PCA) is applied to reduce the dimensionality of the features extracted using
Multi-scale Block LBP (MB-LBP). The experiments were evaluated using ORL, JAFFE and INDIAN
benchmark face recognition databases. It is observed that number of available binary labeled codes
decreases as the scale of the operator and number of grids are increased when MB-LBP is convoluted
with traditional convolution techniques. Moreover, it is noticed that increase in operator scale would
decreases the number of available codes which in turn affects the performance. In order to negate
this effect, this paper proposes to use Modified Convolution (MC) technique to increase the available
binary labeled codes. Experiments on benchmark databases suggested that use of modified convolution method would increase the available codes which in turn increases the performance of the face
verification system.