File System Security by using image Sequence and Eigen Analysis
| Author(s) | : | Animesh Tripathi, Annu Kumar, Mukesh Jadhav, Srishti Mukharya, Asst. Prof. S.M. Shedole |
| Institution | : | Dept of Computer Engg, SIT Lonavala |
| Published In | : | Vol. 3, Issue 3 — March 2016 |
| Page No. | : | 756-761 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational modelfor face recognition is difficult. This paper describes a system for File Security by using an image sequence andsubsequently doing Eigen Analysis over a training dataset of images to minimize the overall error rate and enhance theaccuracy level. Motion information is used to find the moving regions, and probable eye region blobs are extracted bythresholding 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 toextract the edginess image of face. The face recognition is carried out by cumulatively summing up the Euclideandistance between the test face images and the stored database, which shows good discrimination for true and falsesubjects. We can identify at least two broad categories of face recognition systems:1. we want to find a person within alarge data-base of faces (e.g. in a police database). These systems typically return a list of the most likely people in thedatabase. 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, computeretc.) [Multiple images per person are often available for training and real-time recognition is required. The recognizerprovides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% ofthe examples. We use a database of 400 images of 40 individuals which contains quite a high degree of variability inexpression, pose, and facial details.
Animesh Tripathi, Annu Kumar, Mukesh Jadhav, Srishti Mukharya, Asst. Prof. S.M. Shedole, “File System Security by using image Sequence and Eigen Analysis”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 3, pp. 756-761, March 2016.








