20-06-2012, 04:41 PM
FACIAL RECOGNITION
[attachment=24927]
ABSTRACT
Biometrics is the science of measuring and statistically analyzing biological data. By using biometric technology, the body itself becomes a password. Computerized scanners confirm the identity of a person by collecting information on a distinctive biometric attribute, converting it into extremely complex algorithms, then by comparing the data with a digital file in order to determine if there is a match. As biometric systems are deployed as part of identification programs, implementation issues relating to user privacy are paramount. The tie between the actual identity of an individual and the daily use of the biometric is delicate and provokes much debate, particularly relating to privacy and societal issues.
Nowadays terrorism became big threat to world this is due to illegal migration of people by creating fake passport this can be eradicated with help of this technology we can also remove fake ration cards and voter identity cards.
Some of these issues by providing a framework, and by distinguishing between the technology and societal issues this paper seek to clarify.
INTRODUCTION
Definition: A facial recognition system is a computer-driven application for automatically identifying or verifying a person from a digital still or video image. It does that by comparing selected facial features in the live image and a facial database.
It is typically used for security systems and can be compared to other biometrics such as fingerprints or eye iris recognition systems. Facial recognition software is based on the ability to recognize a face and then measure the various features of the face.
FACE RECOGNITION METHODS
Feature based methods: Use geometric features like distance between eyes, their size etc to represent a face. These features are computed using simple correlation filters with expected templates.
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features. Face It defines these landmarks as nodal points. Each human face has approximately 80 nodal points. Some of these measured by the software are:
• Distance between the eyes
• Width of the nose
• Depth of the eye sockets
• The shape of the cheekbones
• The length of the jaw line
. These nodal points are measured creating a numerical code, called a face print, representing the face in the database
IMAGE-BASED METHODS:
Based on ideas like eigenfaces after a large training set of images are collected, principal component analysis is used to compute eigenfaces. Each new face is then characterized by its projection onto this space of principal eigenfaces.
In image processing, processed images of faces can be seen as vectors whose components are the bright nesses of each pixel. The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated to a large set of normalized pictures of faces are called eigenfaces. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigenfaces provide a means of applying data compression to faces for identification purposes.
An eigenvector of a linear transformation is a non zero vector that is either left unaffected or simply
multiplied by a scale factor after the transformation. An eigenspace of a given transformation is the set
of all eigenvectors of that transformation that have the same eigenvalue, together with the zero vector
(Which has no direction).
[attachment=24927]
ABSTRACT
Biometrics is the science of measuring and statistically analyzing biological data. By using biometric technology, the body itself becomes a password. Computerized scanners confirm the identity of a person by collecting information on a distinctive biometric attribute, converting it into extremely complex algorithms, then by comparing the data with a digital file in order to determine if there is a match. As biometric systems are deployed as part of identification programs, implementation issues relating to user privacy are paramount. The tie between the actual identity of an individual and the daily use of the biometric is delicate and provokes much debate, particularly relating to privacy and societal issues.
Nowadays terrorism became big threat to world this is due to illegal migration of people by creating fake passport this can be eradicated with help of this technology we can also remove fake ration cards and voter identity cards.
Some of these issues by providing a framework, and by distinguishing between the technology and societal issues this paper seek to clarify.
INTRODUCTION
Definition: A facial recognition system is a computer-driven application for automatically identifying or verifying a person from a digital still or video image. It does that by comparing selected facial features in the live image and a facial database.
It is typically used for security systems and can be compared to other biometrics such as fingerprints or eye iris recognition systems. Facial recognition software is based on the ability to recognize a face and then measure the various features of the face.
FACE RECOGNITION METHODS
Feature based methods: Use geometric features like distance between eyes, their size etc to represent a face. These features are computed using simple correlation filters with expected templates.
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features. Face It defines these landmarks as nodal points. Each human face has approximately 80 nodal points. Some of these measured by the software are:
• Distance between the eyes
• Width of the nose
• Depth of the eye sockets
• The shape of the cheekbones
• The length of the jaw line
. These nodal points are measured creating a numerical code, called a face print, representing the face in the database
IMAGE-BASED METHODS:
Based on ideas like eigenfaces after a large training set of images are collected, principal component analysis is used to compute eigenfaces. Each new face is then characterized by its projection onto this space of principal eigenfaces.
In image processing, processed images of faces can be seen as vectors whose components are the bright nesses of each pixel. The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated to a large set of normalized pictures of faces are called eigenfaces. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigenfaces provide a means of applying data compression to faces for identification purposes.
An eigenvector of a linear transformation is a non zero vector that is either left unaffected or simply
multiplied by a scale factor after the transformation. An eigenspace of a given transformation is the set
of all eigenvectors of that transformation that have the same eigenvalue, together with the zero vector
(Which has no direction).