29-10-2010, 12:11 PM
[attachment=6995]
SMART QUILL
SEMINAR REPORT
SUBMITTED BY
RENUKADEVI.M
(Reg No: 283576122)
RAJIV GANDHI COLLEGE OF ENGINEERING
ABSTRACT:
Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods . Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. Each human face has specific distinguishable landmarks (or nodal points) that make up the different facial features. It has been known that there is a large number of nodal points on a human face(about 80) and these include the most commonly known - the distance between eyes, width of the nose, depthless of eye sockets, cheekbones, jaw line and chin. There are different methods of facial recognition which involve a series of steps that serve to capturing, analyzing and comparing a face to a database of stored images
This paper describes how to build a simple, yet a complete face recognition system using Principal Component Analysis, a Holistic approach. This method applies linear projection to the original image space to achieve dimensionality reduction. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features known as eigenfaces do not necessarily correspond to features such as ears, eyes and noses. It provides for the ability to learn and later recognize new faces in an unsupervised manner. This method is found to be fast, relatively simple, and works well in a constrained environment.
SMART QUILL
SEMINAR REPORT
SUBMITTED BY
RENUKADEVI.M
(Reg No: 283576122)
RAJIV GANDHI COLLEGE OF ENGINEERING
ABSTRACT:
Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods . Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. Each human face has specific distinguishable landmarks (or nodal points) that make up the different facial features. It has been known that there is a large number of nodal points on a human face(about 80) and these include the most commonly known - the distance between eyes, width of the nose, depthless of eye sockets, cheekbones, jaw line and chin. There are different methods of facial recognition which involve a series of steps that serve to capturing, analyzing and comparing a face to a database of stored images
This paper describes how to build a simple, yet a complete face recognition system using Principal Component Analysis, a Holistic approach. This method applies linear projection to the original image space to achieve dimensionality reduction. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features known as eigenfaces do not necessarily correspond to features such as ears, eyes and noses. It provides for the ability to learn and later recognize new faces in an unsupervised manner. This method is found to be fast, relatively simple, and works well in a constrained environment.
