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Face recognition:
                   Face recognition is one of the most important
biometric which seems to be a good compromise between actuality
and social reception and balances security and privacy well. Face
recognition system fall into two categories: verification and
identification. Face verification is a 1:1 match that compares a face
images against a template face images, whose identity is being
claimed. On the contrary, face identification is a 1: N problem that
compares a query face image against all image templates in a face
database. Face localization, feature extraction, and modeling are the
major issues in automatic facial recognition.
Facial recognition is an effective biometric
attribute/indicator. Different biometric indicators are suited for
different kinds of identification applications due to their variations in
intrusiveness, accuracy, cost, and ease of sensing. Among the six
biometric indicators considered facial features scored the highest
compatibility.
Comparison of various biometric features:
  (a) based on zephyr analysis
  (b) based on MRTD(Machine Readable Travel Documents)compatibility
:

In the 1970s,Goldstein,Harmon,and Lesk1 used 21 specific
subjective markers such as hair color and lip thickness to automate
the recognition. The problem with both of these early solutions was
that the measurements and locations were manually computed.
 In 1988,Kirby and Sirovich applied principle component
analysis, a standard linear algebra technique, to the face recognition
problem. This was considered somewhat of a milestone as it showed
that less than one hundred values were required to accurately code a
suitably aligned and normalized face image.
In1991, Turk and Pent land discovered that while using the
eigenfaces techniques, the residual error could be used to detect
faces in images a discovery that enabled reliable real-time automated
face recognition systems.
A framework for face recognition based attendance
system:
Predominant Approaches:
There are two predominant approaches to the face recognition
problem: 1.Geometric (feature based)
         2.Photometric (view based)
As researcher interest in face recognition continued, Many different
algorithms were developed, three of which have been well studied in
face recognition literature:

                       1. Principal Components Analysis (PCA)
                       2.Linear Discriminant Analysis (LDA)
                       3.Elastic Bunch Graph Matching (EBGM)
Principal Components Analysis (PCA) :

•PCA, commonly referred to as the use of eigenfaces, this technique
pioneered by Kirby and Sirivich in 1988.
•In PCA, the probe and gallery images must be the same size and must
first be normalized.
•The PCA approach is then used to reduce the dimension of the data by
means of data compression basics and reveals the most effective low
dimensional structure of facial patterns. This reduction in dimensions
removes information that is not useful and precisely decomposes the face
structure into orthogonal (uncorrelated) components known as
eigenfaces.
• Each face image may be represented as a weighted sum (feature
     vector) of the eigenfaces, which are stored in a 1D array.
•    A probe image is compared against a gallery image by measuring
     the distance between their respective feature vectors.
• The PCA approach typically requires the full frontal face to            be
presented       each   time   otherwise   the   image   results   in   poor
performance.
•The primary advantage of this technique is that it can reduce the data
    needed to identify the individual to 1/1000th of the data
    presented
Linear Discriminant Analysis (LDA):
                       LDA is a statistical approach for classifying
samples of unknown classes based on training samples with known
classes. This technique aims to maximize between-class (i.e., across
users) variance and minimize within-class (i.e., within user) variance. In
this each block represents a class, there are large variances between
classes, but little variance within classes. When dealing with high
dimensional face data, this technique faces the small sample size
problem that arises where there are a small number of available training
samples compared to the dimensionality of the sample space.
Elastic Bunch Graph Matching (EBGM):
                       EBGM relies on the concept that real face images have many
non-linear characteristics that are not addressed by the linear analysis methods . such
as variations in illumination(outdoor lighting vs. indoor fluorescents), pose (standing
straight vs. leaning over) and expression (smile vs. frown). A Gabor wavelet transform
creates a dynamic link architecture that projects the face onto an elastic grid. The Gabor
jet is a node on the elastic grid, notated by circles on the image below, which describes
the image behavior around a given pixel. It is the result of a convolution of the image
with a Gabor filter, which is used to detect shapes and to extract features using image
processing. Recognition is based on the similarity of the Gabor filter response at each
Gabor node. This biologically-based method using Gabor filters is a process executed in
the visual cortex of higher mammals. The difficulty with this method is the requirement
of accurate landmark localization.
Elastic Bunch Graph Matching
:
1. Digital Image Processing Using MATLAB by Rafael
   C.Gonzalez, Richard E.Woods , Steven L.Eddins.
face recognition system
face recognition system
face recognition system
face recognition system
face recognition system

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face recognition system

  • 1. Face recognition: Face recognition is one of the most important biometric which seems to be a good compromise between actuality and social reception and balances security and privacy well. Face recognition system fall into two categories: verification and identification. Face verification is a 1:1 match that compares a face images against a template face images, whose identity is being claimed. On the contrary, face identification is a 1: N problem that compares a query face image against all image templates in a face database. Face localization, feature extraction, and modeling are the major issues in automatic facial recognition.
  • 2. Facial recognition is an effective biometric attribute/indicator. Different biometric indicators are suited for different kinds of identification applications due to their variations in intrusiveness, accuracy, cost, and ease of sensing. Among the six biometric indicators considered facial features scored the highest compatibility.
  • 3. Comparison of various biometric features: (a) based on zephyr analysis (b) based on MRTD(Machine Readable Travel Documents)compatibility
  • 4. : In the 1970s,Goldstein,Harmon,and Lesk1 used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solutions was that the measurements and locations were manually computed.  In 1988,Kirby and Sirovich applied principle component analysis, a standard linear algebra technique, to the face recognition problem. This was considered somewhat of a milestone as it showed that less than one hundred values were required to accurately code a suitably aligned and normalized face image. In1991, Turk and Pent land discovered that while using the eigenfaces techniques, the residual error could be used to detect faces in images a discovery that enabled reliable real-time automated face recognition systems.
  • 5. A framework for face recognition based attendance system:
  • 6. Predominant Approaches: There are two predominant approaches to the face recognition problem: 1.Geometric (feature based) 2.Photometric (view based)
  • 7. As researcher interest in face recognition continued, Many different algorithms were developed, three of which have been well studied in face recognition literature: 1. Principal Components Analysis (PCA) 2.Linear Discriminant Analysis (LDA) 3.Elastic Bunch Graph Matching (EBGM)
  • 8. Principal Components Analysis (PCA) : •PCA, commonly referred to as the use of eigenfaces, this technique pioneered by Kirby and Sirivich in 1988. •In PCA, the probe and gallery images must be the same size and must first be normalized. •The PCA approach is then used to reduce the dimension of the data by means of data compression basics and reveals the most effective low dimensional structure of facial patterns. This reduction in dimensions removes information that is not useful and precisely decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces.
  • 9. • Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. • A probe image is compared against a gallery image by measuring the distance between their respective feature vectors. • The PCA approach typically requires the full frontal face to be presented each time otherwise the image results in poor performance. •The primary advantage of this technique is that it can reduce the data needed to identify the individual to 1/1000th of the data presented
  • 10. Linear Discriminant Analysis (LDA): LDA is a statistical approach for classifying samples of unknown classes based on training samples with known classes. This technique aims to maximize between-class (i.e., across users) variance and minimize within-class (i.e., within user) variance. In this each block represents a class, there are large variances between classes, but little variance within classes. When dealing with high dimensional face data, this technique faces the small sample size problem that arises where there are a small number of available training samples compared to the dimensionality of the sample space.
  • 11. Elastic Bunch Graph Matching (EBGM): EBGM relies on the concept that real face images have many non-linear characteristics that are not addressed by the linear analysis methods . such as variations in illumination(outdoor lighting vs. indoor fluorescents), pose (standing straight vs. leaning over) and expression (smile vs. frown). A Gabor wavelet transform creates a dynamic link architecture that projects the face onto an elastic grid. The Gabor jet is a node on the elastic grid, notated by circles on the image below, which describes the image behavior around a given pixel. It is the result of a convolution of the image with a Gabor filter, which is used to detect shapes and to extract features using image processing. Recognition is based on the similarity of the Gabor filter response at each Gabor node. This biologically-based method using Gabor filters is a process executed in the visual cortex of higher mammals. The difficulty with this method is the requirement of accurate landmark localization.
  • 13. : 1. Digital Image Processing Using MATLAB by Rafael C.Gonzalez, Richard E.Woods , Steven L.Eddins.