This document describes an image identification system project that uses MATLAB and digital image correlation algorithms. The purpose is to identify persons in investigation departments by storing images in a database and allowing eyewitnesses to match images on screen. The algorithm works by tracking and comparing intensity values between 3D image subsets to minimize differences and identify matches. MATLAB is used as it has powerful graphics and programming tools suitable for this type of image and signal processing project.

1. Image Identifier System
Enroll No. 9910103526
Name of Student Vivek kumar
Name of Supervisor Akanksha Bhardwaj

2. ABSTRACT
• To identify any Person we need some identification
regarding person. In most cases the quality and
resolution of the recorded image segments is poor and
hard to identify a face. To overcome this sort of
problem we are developing software. Identification can
be done in many ways like finger print, eyes, DNA etc.
One of the applications is face identification. The face
is our primary focus of attention in social inters course
playing a major role in conveying identify and emotion.
Although the ability to infer intelligence or character
from facial appearance is suspect, the human ability to
recognize face is remarkable.

3. PURPOSE OF THE PROJECT
• This project is aimed to identify the persons in
any investigation department. Here the
technique is we already store some images of
the persons in our database along with his
details These images are again stored in
another database record so to identify any
persons; eyewitnesses will see the images
that appear on the screen and we will match
that image within our database

4. Use case diagram
operator Login
retriving the images
identifying the images
operator Criminal

5. TECHNOLOGY DESCRIPTION
• MATLAB is widely used in all areas of applied
mathematics, in education and research at
universities, and in the industry
• MATLAB has powerful graphic tools and can
produce nice pictures in both 2D and 3D. It is
also a programming language, and is one of
the easiest programming languages for writing
mathematical programs.

6. • MATLAB also has some tool boxes useful for
signal processing, image processing,
optimization, etc
• MathWorks provides a comprehensive
environment to gain insight into your image
and video data, develop algorithms, and
explore implementation tradeoffs.

7. ALGORITHM USED
• Digital image correlation is an optical method
that employs tracking and image registration
techniques for accurate 2D and 3D
measurements of changes in images. Digital
image correlation (DIC) techniques have been
increasing in popularity, especially in micro-
and nano-scale mechanical testing
applications due to its relative ease of
implementation and use.

8. • The DVC algorithm is able to track full-field
displacement information in the form of voxels
instead of pixels.
• Instead of minimizing a coefficient based on the
summed difference of intensity values in a subset
of a planar image, minimization is done in a 3D-
subset where intensity values corresponding to
(x,y,z) values are compared to a standard and the
summed difference minimized using predictive,
3D displacement fields.

9. CONCLUSIONS
• The experimental DVC method is still being
developed and optimized for speed and
reliability. The first proposition of DVC was in
1999 by the authors Bay, Smith, Fyhrie, and
Saad.[13] This group used X-ray Tomography to
image volumes that could then be correlated
using the DVC algorithm which they
developed in theory. Since then the method
has grown in acceptance and has expanded to
different imaging techniques

10. REFERENCES
• [1]ACKERMANN, F. 1984. Digital image correlation: Performance and
potential application in photogrammetry. Photogrammetric Record 11
(64):429-439.
• [2]BAKER, H H & BINFORD, T D. 1982. A system for automated stereo
mapping. Proceedings of the Symposium of the ISPRS Commission II
Ottawa, Canada.
• [3]FOERSTNER, W. 1982. On the geometric precision of digital correlation.
Int. Archives of Photogrammetry vol. 24 III, Proceedings of the
Symposium of the ISPRS Commission III Helsinki: 176-189.
• [4]FOERSTNER, W. 1984. Quality assessment of object location and point
transfer using digital image correlation techniques. International Archives
of Photogrammetry and Remote Sensing vol. XXV, A3a, Commission III,
Rio de Janeiro.
• [5]GRUEN, A. 1984. Processing of amateur photographs. Presented paper
to the XVth Congress of the ISPRS, Commission V, Rio de Janeiro