File System Security by using image Sequence and Eigen Analysis

Abstract

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model
for face recognition is difficult. This paper describes a system for File Security by using an image sequence and
subsequently doing Eigen Analysis over a training dataset of images to minimize the overall error rate and enhance the
accuracy level. Motion information is used to find the moving regions, and probable eye region blobs are extracted by
thresholding the image. These blobs reduce search space for face verification, which is done by template matching.
Eigen analysis of edginess representation of face is used for face recognition. One dimensional processing is used to
extract the edginess image of face. The face recognition is carried out by cumulatively summing up the Euclidean
distance between the test face images and the stored database, which shows good discrimination for true and false
subjects. We can identify at least two broad categories of face recognition systems:1. we want to find a person within a
large data-base of faces (e.g. in a police database). These systems typically return a list of the most likely people in the
database. Often only one image is available per person. It is usually not necessary for recognition to be done in realtime.
2. We want to identify particular people in real-time (e.g. in a security monitoring system, location Tracking system,
etc.), or we want to allow access to a group of people and deny access to all others (E.g. access to a building, computer
etc.) [Multiple images per person are often available for training and real-time recognition is required. The recognizer
provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of
the examples. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in
expression, pose, and facial details.