23-05-2014, 02:54 PM
PROJECT REPORT On ONLINE VOTING SYSTEM USING FACE RECOGNITON
ONLINE VOTING SYSTEM .pdf (Size: 1.07 MB / Downloads: 33)
ABSTRACT
Online voting system:
In online voting system people can cast their vote through the internet. In order to
prevent voter frauds we use two levels of security. A user id and password are used as the
first level of security. The data entered by the user is verified with the contents of the
database, if the data is correct then the face of the voter is captured by a web camera and
sent to the database. The web page is designed using ASP.NET .The ASP page is then
connected to the Microsoft sql sever database. The ASP page is served from an IIS
server.
In the second level of security the face of the person is verified with the face
present in the database and validated using matlab. The comparison of the two faces is
done using Eigen face recognition algorithm.
The scheme is based on an information theory approach that decomposes face
images into a small set of characteristic feature images called ‘eigenfaces’, which are
actually the principal components of the initial training set of face images. Recognition is
performed by projecting a new image into the subspace spanned by the eigenfaces (‘face
space’) and then classifying the face by comparing its position in the face space with the
positions of the known individuals.
Then the MATLAB coding is converted into a dll file by using a deploy tool
present in the MATLAB. Then the dll file is used in the ASP.NET page to call the
matlab program and display the result in the web page
Developing the application
Microsoft Active Server Pages (ASP) is a server-side scripting technology. ASP is
a technology that Microsoft created to ease the development of interactive Web
applications. With ASP you can use client-side scripts as well as server-side scripts.
Maybe you want to validate user input or access a database. ASP provides solutions for
transaction processing and managing session state. ASP is one of the most successful
language used in web development.
ASP.NET:
ASP.NET was developed in direct response to the problems that developers had
with classic ASP. Since ASP is in such wide use, however, Microsoft ensured that ASP
scripts execute without modification on a machine with the .NET Framework (the ASP
engine, ASP.DLL, is not modified when installing the .NET Framework). Thus, IIS can
house both ASP and ASP.NET scripts on the same machine.
The Problem of Face Recognition:
Face recognition is a very interesting quandary. Ideally a face detection system
should be able to take a new face and return a name identifying that person.
Mathematically, what possible approach would be robust and fairly computationally
economical? If we have a database of people, every face has special features that define
that person. Greg may have a wider forehead, while Jeff has a scar on his right eyebrow
from a rugby match as a young tuck. One technique may be to go through every person in
the database and characterize it by these small features. Another possible approach would
be to take the face image as a whole identity.
Statistically, faces can also be very similar. Walking through a crowd without
glasses, blurry vision can often result in misidentifying someone, thus yielding an
awkward encounter. The statistical similarities between faces gives way to an
identification approach that uses the full face. Using standard image sizes and the same
initial conditions, a system can be built that looks at the statistical relationship of
individual pixels. One person may have a greater distance between his or her eyes then
another, so two regions of pixels will be correlated to one another differently for image
sets of these two people.
Deriving the Eigenface Basis
The eigenface technique is a powerful yet simple solution to the face recognition
dilemma. In fact, it is really the most intuitive way to classify a face. As we have shown,
old techniques focused on particular features of the face. The eigenface technique uses
much more information by classifying faces based on general facial patterns. These
patterns include, but are not limited to, the specific features of the face. By using more
information, eigenface analysis is naturally more effective than feature-based face
recognition.
Eigenfaces are fundamentally nothing more than basis vectors for real faces. This
can be related directly to one of the most fundamental concepts in electrical engineering:
Fourier analysis. Fourier analysis reveals that a sum of weighted sinusoids at differing
frequencies can recompose a signal perfectly! In the same way, a sum of weighted
eigenfaces can seamlessly reconstruct a specific person’s face. Determining what these
eigenfaces are is the crux of this technique. Before finding the eigenfaces, we first need
to collect a set of face images. These face images become our database of known faces.
We will later determine whether or not an unknown face matches any of these known
faces. All face images must be the same size (in pixels), and for our purposes, they must
be grayscale, with values ranging from 0 to 255. Each face image is converted into a
vector Γn of length N (N=imagewidth*imageheight). The most useful face sets have
multiple images per person. This sharply increases accuracy, due to the increased
information available on each known individual. We will call our collection of faces
“face space.” This space is of dimension N.